CUDA C Programming Guide

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CUDA C PROGRAMMING GUIDE
PG-02829-001_v9.1 | December 2017
Design Guide
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CHANGES FROM VERSION 9.0
‣Documented restriction that operator-overloads cannot be __global__ functions in
Operator Function.
‣Removed guidance to break 8-byte shuffles into two 4-byte instructions. 8-byte
shuffle variants are provided since CUDA 9.0. See Warp Shuffle Functions.
‣Passing __restrict__ references to __global__ functions is now supported.
Updated comment in __global__ functions and function templates.
‣Documented CUDA_ENABLE_CRC_CHECK in CUDA Environment Variables.
‣Warp matrix functions [PREVIEW FEATURE] now support matrix products with
m=32, n=8, k=16 and m=8, n=32, k=16 in addition to m=n=k=16.
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TABLE OF CONTENTS
Chapter1.Introduction.........................................................................................1
1.1.From Graphics Processing to General Purpose Parallel Computing............................... 1
1.2.CUDA®: A General-Purpose Parallel Computing Platform and Programming Model.............3
1.3.A Scalable Programming Model.........................................................................4
1.4.Document Structure...................................................................................... 6
Chapter2.Programming Model............................................................................... 8
2.1. Kernels......................................................................................................8
2.2.Thread Hierarchy......................................................................................... 9
2.3.Memory Hierarchy....................................................................................... 11
2.4.Heterogeneous Programming.......................................................................... 13
2.5.Compute Capability..................................................................................... 15
Chapter3.Programming Interface..........................................................................16
3.1.Compilation with NVCC................................................................................ 16
3.1.1.Compilation Workflow.............................................................................17
3.1.1.1.Offline Compilation.......................................................................... 17
3.1.1.2.Just-in-Time Compilation....................................................................17
3.1.2.Binary Compatibility...............................................................................17
3.1.3.PTX Compatibility..................................................................................18
3.1.4.Application Compatibility.........................................................................18
3.1.5.C/C++ Compatibility............................................................................... 19
3.1.6.64-Bit Compatibility............................................................................... 19
3.2.CUDA C Runtime.........................................................................................19
3.2.1. Initialization.........................................................................................20
3.2.2.Device Memory..................................................................................... 20
3.2.3.Shared Memory..................................................................................... 24
3.2.4.Page-Locked Host Memory........................................................................29
3.2.4.1.Portable Memory..............................................................................30
3.2.4.2.Write-Combining Memory....................................................................30
3.2.4.3.Mapped Memory...............................................................................30
3.2.5.Asynchronous Concurrent Execution............................................................ 31
3.2.5.1.Concurrent Execution between Host and Device........................................32
3.2.5.2.Concurrent Kernel Execution............................................................... 32
3.2.5.3.Overlap of Data Transfer and Kernel Execution......................................... 32
3.2.5.4.Concurrent Data Transfers.................................................................. 33
3.2.5.5. Streams......................................................................................... 33
3.2.5.6. Events...........................................................................................37
3.2.5.7.Synchronous Calls.............................................................................38
3.2.6.Multi-Device System............................................................................... 38
3.2.6.1.Device Enumeration.......................................................................... 38
3.2.6.2.Device Selection.............................................................................. 38
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3.2.6.3.Stream and Event Behavior................................................................. 39
3.2.6.4.Peer-to-Peer Memory Access................................................................39
3.2.6.5.Peer-to-Peer Memory Copy..................................................................40
3.2.7.Unified Virtual Address Space................................................................... 41
3.2.8.Interprocess Communication..................................................................... 41
3.2.9.Error Checking......................................................................................42
3.2.10. Call Stack.......................................................................................... 42
3.2.11.Texture and Surface Memory................................................................... 42
3.2.11.1.Texture Memory............................................................................. 43
3.2.11.2.Surface Memory............................................................................. 52
3.2.11.3.CUDA Arrays..................................................................................56
3.2.11.4.Read/Write Coherency..................................................................... 56
3.2.12.Graphics Interoperability........................................................................56
3.2.12.1.OpenGL Interoperability................................................................... 57
3.2.12.2.Direct3D Interoperability...................................................................59
3.2.12.3.SLI Interoperability..........................................................................65
3.3.Versioning and Compatibility.......................................................................... 66
3.4.Compute Modes..........................................................................................67
3.5. Mode Switches........................................................................................... 68
3.6.Tesla Compute Cluster Mode for Windows.......................................................... 68
Chapter4.Hardware Implementation......................................................................70
4.1.SIMT Architecture....................................................................................... 70
4.2.Hardware Multithreading...............................................................................72
Chapter5.Performance Guidelines........................................................................ 74
5.1.Overall Performance Optimization Strategies...................................................... 74
5.2.Maximize Utilization.................................................................................... 74
5.2.1.Application Level...................................................................................74
5.2.2. Device Level........................................................................................ 75
5.2.3.Multiprocessor Level...............................................................................75
5.2.3.1.Occupancy Calculator........................................................................ 77
5.3.Maximize Memory Throughput........................................................................ 79
5.3.1.Data Transfer between Host and Device....................................................... 80
5.3.2.Device Memory Accesses..........................................................................81
5.4.Maximize Instruction Throughput.....................................................................85
5.4.1.Arithmetic Instructions............................................................................85
5.4.2.Control Flow Instructions......................................................................... 89
5.4.3.Synchronization Instruction.......................................................................90
AppendixA.CUDA-Enabled GPUs........................................................................... 91
AppendixB.C Language Extensions........................................................................ 92
B.1.Function Execution Space Specifiers.................................................................92
B.1.1. __device__.......................................................................................... 92
B.1.2. __global__...........................................................................................92
B.1.3. __host__............................................................................................. 93
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B.1.4.__noinline__ and __forceinline__............................................................... 93
B.2.Variable Memory Space Specifiers....................................................................93
B.2.1. __device__.......................................................................................... 94
B.2.2.__constant__........................................................................................94
B.2.3. __shared__.......................................................................................... 94
B.2.4.__managed__....................................................................................... 95
B.2.5. __restrict__......................................................................................... 95
B.3.Built-in Vector Types................................................................................... 97
B.3.1.char, short, int, long, longlong, float, double................................................ 97
B.3.2. dim3..................................................................................................98
B.4.Built-in Variables........................................................................................ 98
B.4.1. gridDim.............................................................................................. 98
B.4.2. blockIdx..............................................................................................98
B.4.3. blockDim.............................................................................................98
B.4.4. threadIdx............................................................................................ 98
B.4.5. warpSize............................................................................................. 99
B.5.Memory Fence Functions...............................................................................99
B.6.Synchronization Functions............................................................................ 101
B.7.Mathematical Functions...............................................................................103
B.8.Texture Functions...................................................................................... 103
B.8.1.Texture Object API............................................................................... 103
B.8.1.1.tex1Dfetch()..................................................................................103
B.8.1.2. tex1D()........................................................................................ 103
B.8.1.3.tex1DLod()....................................................................................103
B.8.1.4.tex1DGrad().................................................................................. 103
B.8.1.5. tex2D()........................................................................................ 104
B.8.1.6.tex2DLod()....................................................................................104
B.8.1.7.tex2DGrad().................................................................................. 104
B.8.1.8. tex3D()........................................................................................ 104
B.8.1.9.tex3DLod()....................................................................................104
B.8.1.10.tex3DGrad().................................................................................104
B.8.1.11.tex1DLayered()............................................................................. 105
B.8.1.12.tex1DLayeredLod().........................................................................105
B.8.1.13.tex1DLayeredGrad()....................................................................... 105
B.8.1.14.tex2DLayered()............................................................................. 105
B.8.1.15.tex2DLayeredLod().........................................................................105
B.8.1.16.tex2DLayeredGrad()....................................................................... 105
B.8.1.17.texCubemap().............................................................................. 106
B.8.1.18.texCubemapLod().......................................................................... 106
B.8.1.19.texCubemapLayered().....................................................................106
B.8.1.20.texCubemapLayeredLod()................................................................ 106
B.8.1.21.tex2Dgather()...............................................................................106
B.8.2.Texture Reference API........................................................................... 107
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B.8.2.1.tex1Dfetch()..................................................................................107
B.8.2.2. tex1D()........................................................................................ 107
B.8.2.3.tex1DLod()....................................................................................108
B.8.2.4.tex1DGrad().................................................................................. 108
B.8.2.5. tex2D()........................................................................................ 108
B.8.2.6.tex2DLod()....................................................................................108
B.8.2.7.tex2DGrad().................................................................................. 108
B.8.2.8. tex3D()........................................................................................ 109
B.8.2.9.tex3DLod()....................................................................................109
B.8.2.10.tex3DGrad().................................................................................109
B.8.2.11.tex1DLayered()............................................................................. 109
B.8.2.12.tex1DLayeredLod().........................................................................110
B.8.2.13.tex1DLayeredGrad()....................................................................... 110
B.8.2.14.tex2DLayered()............................................................................. 110
B.8.2.15.tex2DLayeredLod().........................................................................110
B.8.2.16.tex2DLayeredGrad()....................................................................... 111
B.8.2.17.texCubemap().............................................................................. 111
B.8.2.18.texCubemapLod().......................................................................... 111
B.8.2.19.texCubemapLayered().....................................................................111
B.8.2.20.texCubemapLayeredLod()................................................................ 111
B.8.2.21.tex2Dgather()...............................................................................112
B.9.Surface Functions...................................................................................... 112
B.9.1.Surface Object API............................................................................... 112
B.9.1.1.surf1Dread()..................................................................................112
B.9.1.2. surf1Dwrite................................................................................... 112
B.9.1.3.surf2Dread()..................................................................................113
B.9.1.4. surf2Dwrite()................................................................................. 113
B.9.1.5.surf3Dread()..................................................................................113
B.9.1.6. surf3Dwrite()................................................................................. 113
B.9.1.7.surf1DLayeredread()........................................................................ 114
B.9.1.8.surf1DLayeredwrite()....................................................................... 114
B.9.1.9.surf2DLayeredread()........................................................................ 114
B.9.1.10.surf2DLayeredwrite()......................................................................114
B.9.1.11.surfCubemapread()........................................................................ 115
B.9.1.12.surfCubemapwrite()....................................................................... 115
B.9.1.13.surfCubemapLayeredread()...............................................................115
B.9.1.14.surfCubemapLayeredwrite()..............................................................115
B.9.2.Surface Reference API........................................................................... 116
B.9.2.1.surf1Dread()..................................................................................116
B.9.2.2. surf1Dwrite................................................................................... 116
B.9.2.3.surf2Dread()..................................................................................116
B.9.2.4. surf2Dwrite()................................................................................. 116
B.9.2.5.surf3Dread()..................................................................................117
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B.9.2.6. surf3Dwrite()................................................................................. 117
B.9.2.7.surf1DLayeredread()........................................................................ 117
B.9.2.8.surf1DLayeredwrite()....................................................................... 117
B.9.2.9.surf2DLayeredread()........................................................................ 118
B.9.2.10.surf2DLayeredwrite()......................................................................118
B.9.2.11.surfCubemapread()........................................................................ 118
B.9.2.12.surfCubemapwrite()....................................................................... 118
B.9.2.13.surfCubemapLayeredread()...............................................................119
B.9.2.14.surfCubemapLayeredwrite()..............................................................119
B.10.Read-Only Data Cache Load Function.............................................................119
B.11.Time Function.........................................................................................119
B.12.Atomic Functions..................................................................................... 120
B.12.1.Arithmetic Functions........................................................................... 121
B.12.1.1.atomicAdd().................................................................................121
B.12.1.2.atomicSub()................................................................................. 121
B.12.1.3.atomicExch()................................................................................122
B.12.1.4.atomicMin()................................................................................. 122
B.12.1.5.atomicMax().................................................................................122
B.12.1.6.atomicInc()..................................................................................122
B.12.1.7.atomicDec().................................................................................123
B.12.1.8.atomicCAS().................................................................................123
B.12.2.Bitwise Functions............................................................................... 123
B.12.2.1.atomicAnd().................................................................................123
B.12.2.2.atomicOr().................................................................................. 123
B.12.2.3.atomicXor()................................................................................. 124
B.13.Warp Vote Functions................................................................................. 124
B.14.Warp Match Functions............................................................................... 125
B.14.1. Synopsys.......................................................................................... 125
B.14.2.Description....................................................................................... 125
B.15.Warp Shuffle Functions..............................................................................126
B.15.1. Synopsis........................................................................................... 126
B.15.2.Description....................................................................................... 126
B.15.3.Return Value..................................................................................... 127
B.15.4. Notes.............................................................................................. 128
B.15.5. Examples..........................................................................................128
B.15.5.1.Broadcast of a single value across a warp............................................ 128
B.15.5.2.Inclusive plus-scan across sub-partitions of 8 threads............................... 129
B.15.5.3.Reduction across a warp................................................................. 129
B.16.Warp matrix functions [PREVIEW FEATURE]......................................................129
B.16.1.Description....................................................................................... 130
B.16.2. Example...........................................................................................132
B.17.Profiler Counter Function........................................................................... 132
B.18. Assertion............................................................................................... 133
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B.19.Formatted Output.................................................................................... 134
B.19.1.Format Specifiers............................................................................... 134
B.19.2.Limitations....................................................................................... 135
B.19.3.Associated Host-Side API.......................................................................136
B.19.4. Examples..........................................................................................136
B.20.Dynamic Global Memory Allocation and Operations............................................ 137
B.20.1.Heap Memory Allocation....................................................................... 138
B.20.2.Interoperability with Host Memory API......................................................138
B.20.3. Examples..........................................................................................138
B.20.3.1.Per Thread Allocation.....................................................................139
B.20.3.2.Per Thread Block Allocation............................................................. 140
B.20.3.3.Allocation Persisting Between Kernel Launches...................................... 141
B.21.Execution Configuration............................................................................. 142
B.22.Launch Bounds........................................................................................ 142
B.23.#pragma unroll........................................................................................145
B.24.SIMD Video Instructions..............................................................................145
AppendixC.Cooperative Groups.......................................................................... 147
C.1. Introduction.............................................................................................147
C.2.Intra-block Groups..................................................................................... 148
C.2.1.Thread Groups and Thread Blocks.............................................................148
C.2.2.Tiled Partitions....................................................................................149
C.2.3.Thread Block Tiles............................................................................... 149
C.2.4.Coalesced Groups................................................................................ 150
C.2.5.Uses of Intra-block Cooperative Groups...................................................... 150
C.2.5.1.Discovery Pattern........................................................................... 150
C.2.5.2.Warp-Synchronous Code Pattern..........................................................151
C.2.5.3.Composition.................................................................................. 152
C.3.Grid Synchronization.................................................................................. 152
C.4.Multi-Device Synchronization........................................................................ 154
AppendixD.CUDA Dynamic Parallelism.................................................................. 156
D.1. Introduction.............................................................................................156
D.1.1. Overview........................................................................................... 156
D.1.2. Glossary............................................................................................ 156
D.2.Execution Environment and Memory Model....................................................... 157
D.2.1.Execution Environment.......................................................................... 157
D.2.1.1.Parent and Child Grids..................................................................... 157
D.2.1.2.Scope of CUDA Primitives................................................................. 158
D.2.1.3.Synchronization..............................................................................158
D.2.1.4.Streams and Events.........................................................................158
D.2.1.5.Ordering and Concurrency.................................................................159
D.2.1.6.Device Management........................................................................ 159
D.2.2.Memory Model.................................................................................... 159
D.2.2.1.Coherence and Consistency............................................................... 160
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D.3.Programming Interface................................................................................162
D.3.1.CUDA C/C++ Reference..........................................................................162
D.3.1.1.Device-Side Kernel Launch................................................................ 162
D.3.1.2. Streams....................................................................................... 163
D.3.1.3. Events......................................................................................... 164
D.3.1.4.Synchronization..............................................................................164
D.3.1.5.Device Management........................................................................ 164
D.3.1.6.Memory Declarations....................................................................... 165
D.3.1.7.API Errors and Launch Failures........................................................... 166
D.3.1.8.API Reference................................................................................167
D.3.2.Device-side Launch from PTX.................................................................. 168
D.3.2.1.Kernel Launch APIs......................................................................... 168
D.3.2.2.Parameter Buffer Layout.................................................................. 170
D.3.3.Toolkit Support for Dynamic Parallelism......................................................170
D.3.3.1.Including Device Runtime API in CUDA Code........................................... 170
D.3.3.2.Compiling and Linking......................................................................171
D.4.Programming Guidelines.............................................................................. 171
D.4.1. Basics............................................................................................... 171
D.4.2.Performance.......................................................................................172
D.4.2.1.Synchronization..............................................................................172
D.4.2.2.Dynamic-parallelism-enabled Kernel Overhead........................................ 172
D.4.3.Implementation Restrictions and Limitations................................................173
D.4.3.1. Runtime....................................................................................... 173
AppendixE.Mathematical Functions..................................................................... 176
E.1.Standard Functions.................................................................................... 176
E.2.Intrinsic Functions..................................................................................... 184
AppendixF.C/C++ Language Support.................................................................... 187
F.1.C++11 Language Features............................................................................. 187
F.2.C++14 Language Features............................................................................. 190
F.3. Restrictions.............................................................................................. 190
F.3.1.Host Compiler Extensions........................................................................190
F.3.2.Preprocessor Symbols.............................................................................191
F.3.2.1.__CUDA_ARCH__............................................................................. 191
F.3.3. Qualifiers........................................................................................... 192
F.3.3.1.Device Memory Space Specifiers.......................................................... 192
F.3.3.2.__managed__ Memory Space Specifier...................................................193
F.3.3.3.Volatile Qualifier.............................................................................195
F.3.4. Pointers............................................................................................. 196
F.3.5. Operators........................................................................................... 196
F.3.5.1.Assignment Operator........................................................................ 196
F.3.5.2.Address Operator............................................................................ 196
F.3.6.Run Time Type Information (RTTI)............................................................. 196
F.3.7.Exception Handling............................................................................... 196
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F.3.8.Standard Library...................................................................................196
F.3.9. Functions........................................................................................... 197
F.3.9.1.External Linkage............................................................................. 197
F.3.9.2.Compiler generated functions............................................................. 197
F.3.9.3.Function Parameters........................................................................ 197
F.3.9.4.Static Variables within Function.......................................................... 198
F.3.9.5.Function Pointers............................................................................ 198
F.3.9.6.Function Recursion.......................................................................... 199
F.3.9.7.Friend Functions............................................................................. 199
F.3.9.8.Operator Function........................................................................... 199
F.3.10. Classes............................................................................................. 199
F.3.10.1.Data Members...............................................................................199
F.3.10.2.Function Members..........................................................................199
F.3.10.3.Virtual Functions........................................................................... 199
F.3.10.4.Virtual Base Classes........................................................................199
F.3.10.5.Anonymous Unions......................................................................... 200
F.3.10.6.Windows-Specific........................................................................... 200
F.3.11. Templates......................................................................................... 200
F.3.12.Trigraphs and Digraphs..........................................................................201
F.3.13.Const-qualified variables....................................................................... 201
F.3.14.Deprecation Annotation........................................................................ 202
F.3.15.C++11 Features...................................................................................202
F.3.15.1.Lambda Expressions........................................................................203
F.3.15.2.std::initializer_list..........................................................................204
F.3.15.3.Rvalue references.......................................................................... 204
F.3.15.4.Constexpr functions and function templates.......................................... 204
F.3.15.5.Constexpr variables........................................................................ 205
F.3.15.6.Inline namespaces..........................................................................205
F.3.15.7.thread_local................................................................................. 207
F.3.15.8.__global__ functions and function templates......................................... 207
F.3.15.9.__device__/__constant__/__shared__ variables...................................... 209
F.3.15.10.Defaulted functions.......................................................................209
F.3.16.C++14 Features...................................................................................209
F.3.16.1.Functions with deduced return type....................................................209
F.3.16.2.Variable templates......................................................................... 210
F.3.16.3.[[deprecated]] attribute.................................................................. 211
F.4.Polymorphic Function Wrappers..................................................................... 211
F.5.Experimental Feature: Extended Lambdas.........................................................214
F.5.1.Extended Lambda Type Traits...................................................................216
F.5.2.Extended Lambda Restrictions..................................................................217
F.5.3.Notes on __host__ __device__ lambdas.......................................................225
F.5.4.*this Capture By Value........................................................................... 226
F.5.5.Additional Notes...................................................................................228
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F.6. Code Samples........................................................................................... 230
F.6.1.Data Aggregation Class...........................................................................230
F.6.2.Derived Class...................................................................................... 230
F.6.3.Class Template.....................................................................................231
F.6.4.Function Template................................................................................ 231
F.6.5.Functor Class...................................................................................... 232
AppendixG.Texture Fetching..............................................................................233
G.1.Nearest-Point Sampling............................................................................... 233
G.2.Linear Filtering........................................................................................ 234
G.3. Table Lookup........................................................................................... 235
AppendixH.Compute Capabilities........................................................................ 237
H.1.Features and Technical Specifications............................................................. 237
H.2.Floating-Point Standard...............................................................................241
H.3.Compute Capability 3.x.............................................................................. 242
H.3.1. Architecture....................................................................................... 242
H.3.2.Global Memory....................................................................................243
H.3.3.Shared Memory................................................................................... 245
H.4.Compute Capability 5.x.............................................................................. 246
H.4.1. Architecture....................................................................................... 246
H.4.2.Global Memory....................................................................................247
H.4.3.Shared Memory................................................................................... 247
H.5.Compute Capability 6.x.............................................................................. 251
H.5.1. Architecture....................................................................................... 251
H.5.2.Global Memory....................................................................................251
H.5.3.Shared Memory................................................................................... 251
H.6.Compute Capability 7.x.............................................................................. 252
H.6.1. Architecture....................................................................................... 252
H.6.2.Independent Thread Scheduling............................................................... 252
H.6.3.Global Memory....................................................................................254
H.6.4.Shared Memory................................................................................... 255
AppendixI. Driver API....................................................................................... 256
I.1. Context................................................................................................... 259
I.2. Module.................................................................................................... 260
I.3.Kernel Execution........................................................................................261
I.4.Interoperability between Runtime and Driver APIs............................................... 263
AppendixJ.CUDA Environment Variables............................................................... 264
AppendixK.Unified Memory Programming..............................................................267
K.1.Unified Memory Introduction........................................................................ 267
K.1.1.System Requirements............................................................................ 268
K.1.2.Simplifying GPU Programming..................................................................268
K.1.3.Data Migration and Coherency................................................................. 270
K.1.4.GPU Memory Oversubscription................................................................. 270
K.1.5.Multi-GPU Support................................................................................271
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K.2.Programming Model....................................................................................271
K.2.1.Managed Memory Opt In........................................................................ 271
K.2.1.1.Explicit Allocation Using cudaMallocManaged()........................................ 272
K.2.1.2.Global-Scope Managed Variables Using __managed__.................................273
K.2.2.Coherency and Concurrency.................................................................... 273
K.2.2.1.GPU Exclusive Access To Managed Memory............................................. 273
K.2.2.2.Explicit Synchronization and Logical GPU Activity.....................................274
K.2.2.3.Managing Data Visibility and Concurrent CPU + GPU Access with Streams......... 275
K.2.2.4.Stream Association Examples............................................................. 276
K.2.2.5.Stream Attach With Multithreaded Host Programs.................................... 277
K.2.2.6.Advanced Topic: Modular Programs and Data Access Constraints................... 278
K.2.2.7.Memcpy()/Memset() Behavior With Managed Memory................................ 279
K.2.3.Language Integration............................................................................ 279
K.2.3.1.Host Program Errors with __managed__ Variables.....................................280
K.2.4.Querying Unified Memory Support.............................................................281
K.2.4.1.Device Properties........................................................................... 281
K.2.4.2.Pointer Attributes........................................................................... 281
K.2.5.Advanced Topics.................................................................................. 281
K.2.5.1.Managed Memory with Multi-GPU Programs on pre-6.x Architectures.............. 281
K.2.5.2.Using fork() with Managed Memory...................................................... 282
K.3.Performance Tuning................................................................................... 282
K.3.1.Data Prefetching..................................................................................283
K.3.2.Data Usage Hints................................................................................. 284
K.3.3.Querying Usage Attributes...................................................................... 285
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LIST OF FIGURES
Figure1 Floating-Point Operations per Second for the CPU and GPU ...................................1
Figure2 Memory Bandwidth for the CPU and GPU .........................................................2
Figure3 The GPU Devotes More Transistors to Data Processing ......................................... 2
Figure4 GPU Computing Applications ........................................................................ 4
Figure5 Automatic Scalability ................................................................................. 6
Figure6 Grid of Thread Blocks ...............................................................................10
Figure7 Memory Hierarchy ................................................................................... 12
Figure8 Heterogeneous Programming ...................................................................... 14
Figure9 Matrix Multiplication without Shared Memory .................................................. 26
Figure10 Matrix Multiplication with Shared Memory .....................................................29
Figure11 The Driver API Is Backward but Not Forward Compatible ................................... 67
Figure12 Parent-Child Launch Nesting .................................................................... 158
Figure13 Nearest-Point Sampling Filtering Mode ........................................................234
Figure14 Linear Filtering Mode ............................................................................ 235
Figure15 One-Dimensional Table Lookup Using Linear Filtering ...................................... 236
Figure16 Examples of Global Memory Accesses ......................................................... 245
Figure17 Strided Shared Memory Accesses ...............................................................249
Figure18 Irregular Shared Memory Accesses ............................................................. 250
Figure19 Library Context Management ................................................................... 260
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LIST OF TABLES
Table1 Cubemap Fetch ........................................................................................51
Table2 Throughput of Native Arithmetic Instructions ................................................... 85
Table3 Alignment Requirements .............................................................................97
Table4 New Device-only Launch Implementation Functions .......................................... 167
Table5 Supported API Functions ........................................................................... 167
Table6 Single-Precision Mathematical Standard Library Functions with Maximum ULP Error .... 176
Table7 Double-Precision Mathematical Standard Library Functions with Maximum ULP Error... 180
Table8 Functions Affected by -use_fast_math .......................................................... 184
Table9 Single-Precision Floating-Point Intrinsic Functions .............................................185
Table10 Double-Precision Floating-Point Intrinsic Functions .......................................... 186
Table11 C++11 Language Features ........................................................................ 187
Table12 C++14 Language Features ........................................................................ 190
Table13 Feature Support per Compute Capability ......................................................237
Table14 Technical Specifications per Compute Capability ............................................ 238
Table15 Objects Available in the CUDA Driver API ..................................................... 256
Table16 CUDA Environment Variables ..................................................................... 264
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Chapter1.
INTRODUCTION
1.1.From Graphics Processing to General Purpose
Parallel Computing
Driven by the insatiable market demand for realtime, high-definition 3D graphics,
the programmable Graphic Processor Unit or GPU has evolved into a highly parallel,
multithreaded, manycore processor with tremendous computational horsepower and
very high memory bandwidth, as illustrated by Figure 1 and Figure 2.
Figure1 Floating-Point Operations per Second for the CPU and GPU
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Figure2 Memory Bandwidth for the CPU and GPU
The reason behind the discrepancy in floating-point capability between the CPU and the
GPU is that the GPU is specialized for compute-intensive, highly parallel computation
- exactly what graphics rendering is about - and therefore designed such that more
transistors are devoted to data processing rather than data caching and flow control, as
schematically illustrated by Figure 3.
Cache
ALU
Cont rol
ALU
ALU
ALU
DRAM
CPU
DRAM
Figure3 The GPU Devotes More Transistors to Data Processing
More specifically, the GPU is especially well-suited to address problems that can be
expressed as data-parallel computations - the same program is executed on many data
elements in parallel - with high arithmetic intensity - the ratio of arithmetic operations
to memory operations. Because the same program is executed for each data element,
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there is a lower requirement for sophisticated flow control, and because it is executed on
many data elements and has high arithmetic intensity, the memory access latency can be
hidden with calculations instead of big data caches.
Data-parallel processing maps data elements to parallel processing threads. Many
applications that process large data sets can use a data-parallel programming model
to speed up the computations. In 3D rendering, large sets of pixels and vertices are
mapped to parallel threads. Similarly, image and media processing applications such as
post-processing of rendered images, video encoding and decoding, image scaling, stereo
vision, and pattern recognition can map image blocks and pixels to parallel processing
threads. In fact, many algorithms outside the field of image rendering and processing
are accelerated by data-parallel processing, from general signal processing or physics
simulation to computational finance or computational biology.
1.2.CUDA®: A General-Purpose Parallel Computing
Platform and Programming Model
In November 2006, NVIDIA introduced CUDA®, a general purpose parallel computing
platform and programming model that leverages the parallel compute engine in
NVIDIA GPUs to solve many complex computational problems in a more efficient way
than on a CPU.
CUDA comes with a software environment that allows developers to use C as a high-
level programming language. As illustrated by Figure 4, other languages, application
programming interfaces, or directives-based approaches are supported, such as
FORTRAN, DirectCompute, OpenACC.
Introduction
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Figure4 GPU Computing Applications
CUDA is designed to support various languages and application programming interfaces.
1.3.A Scalable Programming Model
The advent of multicore CPUs and manycore GPUs means that mainstream processor
chips are now parallel systems. Furthermore, their parallelism continues to scale
with Moore's law. The challenge is to develop application software that transparently
scales its parallelism to leverage the increasing number of processor cores, much as
3D graphics applications transparently scale their parallelism to manycore GPUs with
widely varying numbers of cores.
The CUDA parallel programming model is designed to overcome this challenge while
maintaining a low learning curve for programmers familiar with standard programming
languages such as C.
At its core are three key abstractions - a hierarchy of thread groups, shared memories,
and barrier synchronization - that are simply exposed to the programmer as a minimal
set of language extensions.
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These abstractions provide fine-grained data parallelism and thread parallelism,
nested within coarse-grained data parallelism and task parallelism. They guide the
programmer to partition the problem into coarse sub-problems that can be solved
independently in parallel by blocks of threads, and each sub-problem into finer pieces
that can be solved cooperatively in parallel by all threads within the block.
This decomposition preserves language expressivity by allowing threads to cooperate
when solving each sub-problem, and at the same time enables automatic scalability.
Indeed, each block of threads can be scheduled on any of the available multiprocessors
within a GPU, in any order, concurrently or sequentially, so that a compiled CUDA
program can execute on any number of multiprocessors as illustrated by Figure 5, and
only the runtime system needs to know the physical multiprocessor count.
This scalable programming model allows the GPU architecture to span a wide market
range by simply scaling the number of multiprocessors and memory partitions: from
the high-performance enthusiast GeForce GPUs and professional Quadro and Tesla
computing products to a variety of inexpensive, mainstream GeForce GPUs (see CUDA-
Enabled GPUs for a list of all CUDA-enabled GPUs).
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GPU w ith 2 SMs
SM 1
SM 0
GPU w ith 4 SMs
SM 1
SM 0
SM 3
SM 2
Block
5
Block 6
Mult ithreaded CUDA Program
Block 0
Block 1
Block
2
Block 3
Block 4
Block 5
Block
6
Block 7
Block 1
Block 0
Block 3
Block
2
Block 5
Block
4
Block 7
Block 6
Block 0
Block
1
Block
2
Block 3
Block 4
Block
5
Block 6
Block 7
A GPU is built around an array of Streaming Multiprocessors (SMs) (see Hardware
Implementation for more details). A multithreaded program is partitioned into blocks
of threads that execute independently from each other, so that a GPU with more
multiprocessors will automatically execute the program in less time than a GPU with
fewer multiprocessors.
Figure5 Automatic Scalability
1.4.Document Structure
This document is organized into the following chapters:
‣Chapter Introduction is a general introduction to CUDA.
‣Chapter Programming Model outlines the CUDA programming model.
‣Chapter Programming Interface describes the programming interface.
‣Chapter Hardware Implementation describes the hardware implementation.
‣Chapter Performance Guidelines gives some guidance on how to achieve maximum
performance.
‣Appendix CUDA-Enabled GPUs lists all CUDA-enabled devices.
‣Appendix C Language Extensions is a detailed description of all extensions to the C
language.
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‣Appendix Cooperative Groups describes synchronization primitives for various
groups of CUDA threads.
‣Appendix CUDA Dynamic Parallelism describes how to launch and synchronize
one kernel from another.
‣Appendix Mathematical Functions lists the mathematical functions supported in
CUDA.
‣Appendix C/C++ Language Support lists the C++ features supported in device code.
‣Appendix Texture Fetching gives more details on texture fetching
‣Appendix Compute Capabilities gives the technical specifications of various devices,
as well as more architectural details.
‣Appendix Driver API introduces the low-level driver API.
‣Appendix CUDA Environment Variables lists all the CUDA environment variables.
‣Appendix Unified Memory Programming introduces the Unified Memory
programming model.
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Chapter2.
PROGRAMMING MODEL
This chapter introduces the main concepts behind the CUDA programming model by
outlining how they are exposed in C. An extensive description of CUDA C is given in
Programming Interface.
Full code for the vector addition example used in this chapter and the next can be found
in the vectorAdd CUDA sample.
2.1.Kernels
CUDA C extends C by allowing the programmer to define C functions, called kernels,
that, when called, are executed N times in parallel by N different CUDA threads, as
opposed to only once like regular C functions.
A kernel is defined using the __global__ declaration specifier and the number of
CUDA threads that execute that kernel for a given kernel call is specified using a new
<<<...>>> execution configuration syntax (see C Language Extensions). Each thread
that executes the kernel is given a unique thread ID that is accessible within the kernel
through the built-in threadIdx variable.
As an illustration, the following sample code adds two vectors A and B of size N and
stores the result into vector C:
// Kernel definition
__global__ void VecAdd(float* A, float* B, float* C)
{
int i = threadIdx.x;
C[i] = A[i] + B[i];
}
int main()
{
...
// Kernel invocation with N threads
VecAdd<<<1, N>>>(A, B, C);
...
}
Here, each of the N threads that execute VecAdd() performs one pair-wise addition.
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2.2.Thread Hierarchy
For convenience, threadIdx is a 3-component vector, so that threads can be identified
using a one-dimensional, two-dimensional, or three-dimensional thread index, forming
a one-dimensional, two-dimensional, or three-dimensional block of threads, called a
thread block. This provides a natural way to invoke computation across the elements in a
domain such as a vector, matrix, or volume.
The index of a thread and its thread ID relate to each other in a straightforward way:
For a one-dimensional block, they are the same; for a two-dimensional block of size (Dx,
Dy),the thread ID of a thread of index (x, y) is (x + y Dx); for a three-dimensional block of
size (Dx, Dy, Dz), the thread ID of a thread of index (x, y, z) is (x + y Dx + z Dx Dy).
As an example, the following code adds two matrices A and B of size NxN and stores the
result into matrix C:
// Kernel definition
__global__ void MatAdd(float A[N][N], float B[N][N],
float C[N][N])
{
int i = threadIdx.x;
int j = threadIdx.y;
C[i][j] = A[i][j] + B[i][j];
}
int main()
{
...
// Kernel invocation with one block of N * N * 1 threads
int numBlocks = 1;
dim3 threadsPerBlock(N, N);
MatAdd<<<numBlocks, threadsPerBlock>>>(A, B, C);
...
}
There is a limit to the number of threads per block, since all threads of a block are
expected to reside on the same processor core and must share the limited memory
resources of that core. On current GPUs, a thread block may contain up to 1024 threads.
However, a kernel can be executed by multiple equally-shaped thread blocks, so that the
total number of threads is equal to the number of threads per block times the number of
blocks.
Blocks are organized into a one-dimensional, two-dimensional, or three-dimensional
grid of thread blocks as illustrated by Figure 6. The number of thread blocks in a grid is
usually dictated by the size of the data being processed or the number of processors in
the system, which it can greatly exceed.
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Grid
Block (1, 1)
Thr
e
ad
(0, 0)
Thread
(
1
, 0)
Thr
ead (
2
, 0)
Thr
ead
(3, 0)
Thr
ead (0, 1)
Thread
(
1
,
1
)
Thr
ead (
2
,
1
)
Thr
ead
(3, 1)
Thr
ead (0, 2)
Thr
ead (1
, 2)
Thr
ead (
2
,
2
)
Thr
ead
(3
, 2)
Block
(2, 1)
Block
(1
, 1)
Block
(0, 1)
Block
(2,
0
)
Block
(1
, 0)
Block
(0,
0
)
Figure6 Grid of Thread Blocks
The number of threads per block and the number of blocks per grid specified in the
<<<...>>> syntax can be of type int or dim3. Two-dimensional blocks or grids can be
specified as in the example above.
Each block within the grid can be identified by a one-dimensional, two-dimensional,
or three-dimensional index accessible within the kernel through the built-in blockIdx
variable. The dimension of the thread block is accessible within the kernel through the
built-in blockDim variable.
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Extending the previous MatAdd() example to handle multiple blocks, the code becomes
as follows.
// Kernel definition
__global__ void MatAdd(float A[N][N], float B[N][N],
float C[N][N])
{
int i = blockIdx.x * blockDim.x + threadIdx.x;
int j = blockIdx.y * blockDim.y + threadIdx.y;
if (i < N && j < N)
C[i][j] = A[i][j] + B[i][j];
}
int main()
{
...
// Kernel invocation
dim3 threadsPerBlock(16, 16);
dim3 numBlocks(N / threadsPerBlock.x, N / threadsPerBlock.y);
MatAdd<<<numBlocks, threadsPerBlock>>>(A, B, C);
...
}
A thread block size of 16x16 (256 threads), although arbitrary in this case, is a common
choice. The grid is created with enough blocks to have one thread per matrix element
as before. For simplicity, this example assumes that the number of threads per grid in
each dimension is evenly divisible by the number of threads per block in that dimension,
although that need not be the case.
Thread blocks are required to execute independently: It must be possible to execute
them in any order, in parallel or in series. This independence requirement allows thread
blocks to be scheduled in any order across any number of cores as illustrated by Figure
5, enabling programmers to write code that scales with the number of cores.
Threads within a block can cooperate by sharing data through some shared memory and
by synchronizing their execution to coordinate memory accesses. More precisely, one
can specify synchronization points in the kernel by calling the __syncthreads()
intrinsic function; __syncthreads() acts as a barrier at which all threads in the
block must wait before any is allowed to proceed. Shared Memory gives an example of
using shared memory. In addition to __syncthreads(), the Cooperative Groups API
provides a rich set of thread-synchronization primitives.
For efficient cooperation, the shared memory is expected to be a low-latency memory
near each processor core (much like an L1 cache) and __syncthreads() is expected to
be lightweight.
2.3.Memory Hierarchy
CUDA threads may access data from multiple memory spaces during their execution
as illustrated by Figure 7. Each thread has private local memory. Each thread block has
shared memory visible to all threads of the block and with the same lifetime as the block.
All threads have access to the same global memory.
There are also two additional read-only memory spaces accessible by all threads: the
constant and texture memory spaces. The global, constant, and texture memory spaces
are optimized for different memory usages (see Device Memory Accesses). Texture
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memory also offers different addressing modes, as well as data filtering, for some
specific data formats (see Texture and Surface Memory).
The global, constant, and texture memory spaces are persistent across kernel launches
by the same application.
Global memory
Grid 0
Block (2, 1)
Block
(1, 1)
Block
(
0
, 1)
Block (2,
0)
Block
(1, 0)
Block
(
0
,
0
)
Grid 1
Block
(1, 1)
Block
(1,
0
)
Block
(1,
2
)
Block
(0, 1)
Block
(0,
0
)
Block
(0,
2
)
Thread
Block
Per
-
block shared
mem ory
Thread
Per
-t hread local
mem ory
Figure7 Memory Hierarchy
Programming Model
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2.4.Heterogeneous Programming
As illustrated by Figure 8, the CUDA programming model assumes that the CUDA
threads execute on a physically separate device that operates as a coprocessor to the host
running the C program. This is the case, for example, when the kernels execute on a
GPU and the rest of the C program executes on a CPU.
The CUDA programming model also assumes that both the host and the device
maintain their own separate memory spaces in DRAM, referred to as host memory and
device memory, respectively. Therefore, a program manages the global, constant, and
texture memory spaces visible to kernels through calls to the CUDA runtime (described
in Programming Interface). This includes device memory allocation and deallocation as
well as data transfer between host and device memory.
Unified Memory provides managed memory to bridge the host and device memory
spaces. Managed memory is accessible from all CPUs and GPUs in the system as a
single, coherent memory image with a common address space. This capability enables
oversubscription of device memory and can greatly simplify the task of porting
applications by eliminating the need to explicitly mirror data on host and device. See
Unified Memory Programming for an introduction to Unified Memory.”
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Device
Grid 0
Block
(2, 1)
Block (
1, 1)
Block
(0
, 1)
Block
(2, 0)
Block (
1,
0
)
Block
(0
, 0
)
Host
C Program
Sequential
Execution
Serial code
Parallel kernel
Kernel0< < < > > > ()
Serial code
Parallel kernel
Kernel1< < < > > > ()
Host
Device
Grid 1
Block
(1
, 1)
Block
(1
, 0
)
Block
(1
, 2
)
Block
(
0
, 1)
Block
(
0
, 0
)
Block
(
0
, 2
)
Serial code executes on the host while parallel code executes on the device.
Figure8 Heterogeneous Programming
Programming Model
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2.5.Compute Capability
The compute capability of a device is represented by a version number, also sometimes
called its "SM version". This version number identifies the features supported by the
GPU hardware and is used by applications at runtime to determine which hardware
features and/or instructions are available on the present GPU.
The compute capability comprises a major revision number X and a minor revision
number Y and is denoted by X.Y.
Devices with the same major revision number are of the same core architecture. The
major revision number is 7 for devices based on the Volta architecture, 6 for devices
based on the Pascal architecture, 5 for devices based on the Maxwell architecture, 3 for
devices based on the Kepler architecture, 2 for devices based on the Fermi architecture,
and 1 for devices based on the Tesla architecture.
The minor revision number corresponds to an incremental improvement to the core
architecture, possibly including new features.
CUDA-Enabled GPUs lists of all CUDA-enabled devices along with their compute
capability. Compute Capabilities gives the technical specifications of each compute
capability.
The compute capability version of a particular GPU should not be confused with the
CUDA version (e.g., CUDA 7.5, CUDA 8, CUDA 9), which is the version of the CUDA
software platform. The CUDA platform is used by application developers to create
applications that run on many generations of GPU architectures, including future
GPU architectures yet to be invented. While new versions of the CUDA platform often
add native support for a new GPU architecture by supporting the compute capability
version of that architecture, new versions of the CUDA platform typically also include
software features that are independent of hardware generation.
The Tesla and Fermi architectures are no longer supported starting with CUDA 7.0 and
CUDA 9.0, respectively.
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CUDA C Programming Guide PG-02829-001_v9.1|16
Chapter3.
PROGRAMMING INTERFACE
CUDA C provides a simple path for users familiar with the C programming language to
easily write programs for execution by the device.
It consists of a minimal set of extensions to the C language and a runtime library.
The core language extensions have been introduced in Programming Model. They allow
programmers to define a kernel as a C function and use some new syntax to specify the
grid and block dimension each time the function is called. A complete description of all
extensions can be found in C Language Extensions. Any source file that contains some of
these extensions must be compiled with nvcc as outlined in Compilation with NVCC.
The runtime is introduced in Compilation Workflow. It provides C functions that
execute on the host to allocate and deallocate device memory, transfer data between host
memory and device memory, manage systems with multiple devices, etc. A complete
description of the runtime can be found in the CUDA reference manual.
The runtime is built on top of a lower-level C API, the CUDA driver API, which is
also accessible by the application. The driver API provides an additional level of
control by exposing lower-level concepts such as CUDA contexts - the analogue of host
processes for the device - and CUDA modules - the analogue of dynamically loaded
libraries for the device. Most applications do not use the driver API as they do not
need this additional level of control and when using the runtime, context and module
management are implicit, resulting in more concise code. The driver API is introduced
in Driver API and fully described in the reference manual.
3.1.Compilation with NVCC
Kernels can be written using the CUDA instruction set architecture, called PTX, which
is described in the PTX reference manual. It is however usually more effective to use a
high-level programming language such as C. In both cases, kernels must be compiled
into binary code by nvcc to execute on the device.
nvcc is a compiler driver that simplifies the process of compiling C or PTX code: It
provides simple and familiar command line options and executes them by invoking the
collection of tools that implement the different compilation stages. This section gives
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CUDA C Programming Guide PG-02829-001_v9.1|17
an overview of nvcc workflow and command options. A complete description can be
found in the nvcc user manual.
3.1.1.Compilation Workflow
3.1.1.1.Offline Compilation
Source files compiled with nvcc can include a mix of host code (i.e., code that executes
on the host) and device code (i.e., code that executes on the device). nvcc's basic
workflow consists in separating device code from host code and then:
‣compiling the device code into an assembly form (PTX code) and/or binary form
(cubin object),
‣and modifying the host code by replacing the <<<...>>> syntax introduced in
Kernels (and described in more details in Execution Configuration) by the necessary
CUDA C runtime function calls to load and launch each compiled kernel from the
PTX code and/or cubin object.
The modified host code is output either as C code that is left to be compiled using
another tool or as object code directly by letting nvcc invoke the host compiler during
the last compilation stage.
Applications can then:
‣Either link to the compiled host code (this is the most common case),
‣Or ignore the modified host code (if any) and use the CUDA driver API (see Driver
API) to load and execute the PTX code or cubin object.
3.1.1.2.Just-in-Time Compilation
Any PTX code loaded by an application at runtime is compiled further to binary code
by the device driver. This is called just-in-time compilation. Just-in-time compilation
increases application load time, but allows the application to benefit from any new
compiler improvements coming with each new device driver. It is also the only way
for applications to run on devices that did not exist at the time the application was
compiled, as detailed in Application Compatibility.
When the device driver just-in-time compiles some PTX code for some application, it
automatically caches a copy of the generated binary code in order to avoid repeating
the compilation in subsequent invocations of the application. The cache - referred to as
compute cache - is automatically invalidated when the device driver is upgraded, so that
applications can benefit from the improvements in the new just-in-time compiler built
into the device driver.
Environment variables are available to control just-in-time compilation as described in
CUDA Environment Variables
3.1.2.Binary Compatibility
Binary code is architecture-specific. A cubin object is generated using the compiler
option -code that specifies the targeted architecture: For example, compiling with
-code=sm_35 produces binary code for devices of compute capability 3.5. Binary
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compatibility is guaranteed from one minor revision to the next one, but not from one
minor revision to the previous one or across major revisions. In other words, a cubin
object generated for compute capability X.y will only execute on devices of compute
capability X.z where z≥y.
3.1.3.PTX Compatibility
Some PTX instructions are only supported on devices of higher compute capabilities.
For example, Warp Shuffle Functions are only supported on devices of compute
capability 3.0 and above. The -arch compiler option specifies the compute capability
that is assumed when compiling C to PTX code. So, code that contains warp shuffle, for
example, must be compiled with -arch=compute_30 (or higher).
PTX code produced for some specific compute capability can always be compiled to
binary code of greater or equal compute capability. Note that a binary compiled from an
earlier PTX version may not make use of some hardware features. For example, a binary
targeting devices of compute capability 7.0 (Volta) compiled from PTX generated for
compute capability 6.0 (Pascal) will not make use of Tensor Core instructions, since these
were not available on Pascal. As a result, the final binary may perform worse than would
be possible if the binary were generated using the latest version of PTX.
3.1.4.Application Compatibility
To execute code on devices of specific compute capability, an application must load
binary or PTX code that is compatible with this compute capability as described in
Binary Compatibility and PTX Compatibility. In particular, to be able to execute code
on future architectures with higher compute capability (for which no binary code can be
generated yet), an application must load PTX code that will be just-in-time compiled for
these devices (see Just-in-Time Compilation).
Which PTX and binary code gets embedded in a CUDA C application is controlled by
the -arch and -code compiler options or the -gencode compiler option as detailed in
the nvcc user manual. For example,
nvcc x.cu
-gencode arch=compute_35,code=sm_35
-gencode arch=compute_50,code=sm_50
-gencode arch=compute_60,code=\'compute_60,sm_60\'
embeds binary code compatible with compute capability 3.5 and 5.0 (first and second
-gencode options) and PTX and binary code compatible with compute capability 6.0
(third -gencode option).
Host code is generated to automatically select at runtime the most appropriate code to
load and execute, which, in the above example, will be:
‣3.5 binary code for devices with compute capability 3.5 and 3.7,
‣5.0 binary code for devices with compute capability 5.0 and 5.2,
‣6.0 binary code for devices with compute capability 6.0 and 6.1,
‣PTX code which is compiled to binary code at runtime for devices with compute
capability 7.0 and higher.
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x.cu can have an optimized code path that uses warp shuffle operations, for example,
which are only supported in devices of compute capability 3.0 and higher. The
__CUDA_ARCH__ macro can be used to differentiate various code paths based on
compute capability. It is only defined for device code. When compiling with -
arch=compute_35 for example, __CUDA_ARCH__ is equal to 350.
Applications using the driver API must compile code to separate files and explicitly load
and execute the most appropriate file at runtime.
The Volta architecture introduces Independent Thread Scheduling which changes the
way threads are scheduled on the GPU. For code relying on specific behavior of SIMT
scheduling in previous architecures, Independent Thread Scheduling may alter the set of
participating threads, leading to incorrect results. To aid migration while implementing
the corrective actions detailed in Independent Thread Scheduling, Volta developers
can opt-in to Pascal's thread scheduling with the compiler option combination -
arch=compute_60 -code=sm_70.
The nvcc user manual lists various shorthand for the -arch, -code, and -gencode
compiler options. For example, -arch=sm_35 is a shorthand for -arch=compute_35 -
code=compute_35,sm_35 (which is the same as -gencode arch=compute_35,code=
\'compute_35,sm_35\').
3.1.5.C/C++ Compatibility
The front end of the compiler processes CUDA source files according to C++ syntax
rules. Full C++ is supported for the host code. However, only a subset of C++ is fully
supported for the device code as described in C/C++ Language Support.
3.1.6.64-Bit Compatibility
The 64-bit version of nvcc compiles device code in 64-bit mode (i.e., pointers are 64-bit).
Device code compiled in 64-bit mode is only supported with host code compiled in 64-
bit mode.
Similarly, the 32-bit version of nvcc compiles device code in 32-bit mode and device
code compiled in 32-bit mode is only supported with host code compiled in 32-bit mode.
The 32-bit version of nvcc can compile device code in 64-bit mode also using the -m64
compiler option.
The 64-bit version of nvcc can compile device code in 32-bit mode also using the -m32
compiler option.
3.2.CUDA C Runtime
The runtime is implemented in the cudart library, which is linked to the application,
either statically via cudart.lib or libcudart.a, or dynamically via cudart.dll or
libcudart.so. Applications that require cudart.dll and/or cudart.so for dynamic
linking typically include them as part of the application installation package.
All its entry points are prefixed with cuda.
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As mentioned in Heterogeneous Programming, the CUDA programming model
assumes a system composed of a host and a device, each with their own separate
memory. Device Memory gives an overview of the runtime functions used to manage
device memory.
Shared Memory illustrates the use of shared memory, introduced in Thread Hierarchy,
to maximize performance.
Page-Locked Host Memory introduces page-locked host memory that is required to
overlap kernel execution with data transfers between host and device memory.
Asynchronous Concurrent Execution describes the concepts and API used to enable
asynchronous concurrent execution at various levels in the system.
Multi-Device System shows how the programming model extends to a system with
multiple devices attached to the same host.
Error Checking describes how to properly check the errors generated by the runtime.
Call Stack mentions the runtime functions used to manage the CUDA C call stack.
Texture and Surface Memory presents the texture and surface memory spaces that
provide another way to access device memory; they also expose a subset of the GPU
texturing hardware.
Graphics Interoperability introduces the various functions the runtime provides to
interoperate with the two main graphics APIs, OpenGL and Direct3D.
3.2.1.Initialization
There is no explicit initialization function for the runtime; it initializes the first time a
runtime function is called (more specifically any function other than functions from the
device and version management sections of the reference manual). One needs to keep
this in mind when timing runtime function calls and when interpreting the error code
from the first call into the runtime.
During initialization, the runtime creates a CUDA context for each device in the system
(see Context for more details on CUDA contexts). This context is the primary context for
this device and it is shared among all the host threads of the application. As part of this
context creation, the device code is just-in-time compiled if necessary (see Just-in-Time
Compilation) and loaded into device memory. This all happens under the hood and the
runtime does not expose the primary context to the application.
When a host thread calls cudaDeviceReset(), this destroys the primary context of the
device the host thread currently operates on (i.e., the current device as defined in Device
Selection). The next runtime function call made by any host thread that has this device
as current will create a new primary context for this device.
3.2.2.Device Memory
As mentioned in Heterogeneous Programming, the CUDA programming model
assumes a system composed of a host and a device, each with their own separate
memory. Kernels operate out of device memory, so the runtime provides functions to
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allocate, deallocate, and copy device memory, as well as transfer data between host
memory and device memory.
Device memory can be allocated either as linear memory or as CUDA arrays.
CUDA arrays are opaque memory layouts optimized for texture fetching. They are
described in Texture and Surface Memory.
Linear memory exists on the device in a 40-bit address space, so separately allocated
entities can reference one another via pointers, for example, in a binary tree.
Linear memory is typically allocated using cudaMalloc() and freed using cudaFree()
and data transfer between host memory and device memory are typically done using
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cudaMemcpy(). In the vector addition code sample of Kernels, the vectors need to be
copied from host memory to device memory:
// Device code
__global__ void VecAdd(float* A, float* B, float* C, int N)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < N)
C[i] = A[i] + B[i];
}
// Host code
int main()
{
int N = ...;
size_t size = N * sizeof(float);
// Allocate input vectors h_A and h_B in host memory
float* h_A = (float*)malloc(size);
float* h_B = (float*)malloc(size);
// Initialize input vectors
...
// Allocate vectors in device memory
float* d_A;
cudaMalloc(&d_A, size);
float* d_B;
cudaMalloc(&d_B, size);
float* d_C;
cudaMalloc(&d_C, size);
// Copy vectors from host memory to device memory
cudaMemcpy(d_A, h_A, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_B, h_B, size, cudaMemcpyHostToDevice);
// Invoke kernel
int threadsPerBlock = 256;
int blocksPerGrid =
(N + threadsPerBlock - 1) / threadsPerBlock;
VecAdd<<<blocksPerGrid, threadsPerBlock>>>(d_A, d_B, d_C, N);
// Copy result from device memory to host memory
// h_C contains the result in host memory
cudaMemcpy(h_C, d_C, size, cudaMemcpyDeviceToHost);
// Free device memory
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
// Free host memory
...
}
Linear memory can also be allocated through cudaMallocPitch() and
cudaMalloc3D(). These functions are recommended for allocations of 2D or 3D
arrays as it makes sure that the allocation is appropriately padded to meet the
alignment requirements described in Device Memory Accesses, therefore ensuring best
performance when accessing the row addresses or performing copies between 2D arrays
and other regions of device memory (using the cudaMemcpy2D() and cudaMemcpy3D()
functions). The returned pitch (or stride) must be used to access array elements. The
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following code sample allocates a width x height 2D array of floating-point values and
shows how to loop over the array elements in device code:
// Host code
int width = 64, height = 64;
float* devPtr;
size_t pitch;
cudaMallocPitch(&devPtr, &pitch,
width * sizeof(float), height);
MyKernel<<<100, 512>>>(devPtr, pitch, width, height);
// Device code
__global__ void MyKernel(float* devPtr,
size_t pitch, int width, int height)
{
for (int r = 0; r < height; ++r) {
float* row = (float*)((char*)devPtr + r * pitch);
for (int c = 0; c < width; ++c) {
float element = row[c];
}
}
}
The following code sample allocates a width x height x depth 3D array of floating-
point values and shows how to loop over the array elements in device code:
// Host code
int width = 64, height = 64, depth = 64;
cudaExtent extent = make_cudaExtent(width * sizeof(float),
height, depth);
cudaPitchedPtr devPitchedPtr;
cudaMalloc3D(&devPitchedPtr, extent);
MyKernel<<<100, 512>>>(devPitchedPtr, width, height, depth);
// Device code
__global__ void MyKernel(cudaPitchedPtr devPitchedPtr,
int width, int height, int depth)
{
char* devPtr = devPitchedPtr.ptr;
size_t pitch = devPitchedPtr.pitch;
size_t slicePitch = pitch * height;
for (int z = 0; z < depth; ++z) {
char* slice = devPtr + z * slicePitch;
for (int y = 0; y < height; ++y) {
float* row = (float*)(slice + y * pitch);
for (int x = 0; x < width; ++x) {
float element = row[x];
}
}
}
}
The reference manual lists all the various functions used to copy memory between
linear memory allocated with cudaMalloc(), linear memory allocated with
cudaMallocPitch() or cudaMalloc3D(), CUDA arrays, and memory allocated for
variables declared in global or constant memory space.
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The following code sample illustrates various ways of accessing global variables via the
runtime API:
__constant__ float constData[256];
float data[256];
cudaMemcpyToSymbol(constData, data, sizeof(data));
cudaMemcpyFromSymbol(data, constData, sizeof(data));
__device__ float devData;
float value = 3.14f;
cudaMemcpyToSymbol(devData, &value, sizeof(float));
__device__ float* devPointer;
float* ptr;
cudaMalloc(&ptr, 256 * sizeof(float));
cudaMemcpyToSymbol(devPointer, &ptr, sizeof(ptr));
cudaGetSymbolAddress() is used to retrieve the address pointing to the memory
allocated for a variable declared in global memory space. The size of the allocated
memory is obtained through cudaGetSymbolSize().
3.2.3.Shared Memory
As detailed in Variable Memory Space Specifiers shared memory is allocated using the
__shared__ memory space specifier.
Shared memory is expected to be much faster than global memory as mentioned in
Thread Hierarchy and detailed in Shared Memory. Any opportunity to replace global
memory accesses by shared memory accesses should therefore be exploited as illustrated
by the following matrix multiplication example.
The following code sample is a straightforward implementation of matrix multiplication
that does not take advantage of shared memory. Each thread reads one row of A and one
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column of B and computes the corresponding element of C as illustrated in Figure 9. A is
therefore read B.width times from global memory and B is read A.height times.
// Matrices are stored in row-major order:
// M(row, col) = *(M.elements + row * M.width + col)
typedef struct {
int width;
int height;
float* elements;
} Matrix;
// Thread block size
#define BLOCK_SIZE 16
// Forward declaration of the matrix multiplication kernel
__global__ void MatMulKernel(const Matrix, const Matrix, Matrix);
// Matrix multiplication - Host code
// Matrix dimensions are assumed to be multiples of BLOCK_SIZE
void MatMul(const Matrix A, const Matrix B, Matrix C)
{
// Load A and B to device memory
Matrix d_A;
d_A.width = A.width; d_A.height = A.height;
size_t size = A.width * A.height * sizeof(float);
cudaMalloc(&d_A.elements, size);
cudaMemcpy(d_A.elements, A.elements, size,
cudaMemcpyHostToDevice);
Matrix d_B;
d_B.width = B.width; d_B.height = B.height;
size = B.width * B.height * sizeof(float);
cudaMalloc(&d_B.elements, size);
cudaMemcpy(d_B.elements, B.elements, size,
cudaMemcpyHostToDevice);
// Allocate C in device memory
Matrix d_C;
d_C.width = C.width; d_C.height = C.height;
size = C.width * C.height * sizeof(float);
cudaMalloc(&d_C.elements, size);
// Invoke kernel
dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
dim3 dimGrid(B.width / dimBlock.x, A.height / dimBlock.y);
MatMulKernel<<<dimGrid, dimBlock>>>(d_A, d_B, d_C);
// Read C from device memory
cudaMemcpy(C.elements, Cd.elements, size,
cudaMemcpyDeviceToHost);
// Free device memory
cudaFree(d_A.elements);
cudaFree(d_B.elements);
cudaFree(d_C.elements);
}
// Matrix multiplication kernel called by MatMul()
__global__ void MatMulKernel(Matrix A, Matrix B, Matrix C)
{
// Each thread computes one element of C
// by accumulating results into Cvalue
float Cvalue = 0;
int row = blockIdx.y * blockDim.y + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
for (int e = 0; e < A.width; ++e)
Cvalue += A.elements[row * A.width + e]
* B.elements[e * B.width + col];
C.elements[row * C.width + col] = Cvalue;
}
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A
B
C
B. w idt h
A.w idth
0
col
A.height
B. height
B. w idt h
-
1
row
0
A.height
-1
Figure9 Matrix Multiplication without Shared Memory
The following code sample is an implementation of matrix multiplication that does take
advantage of shared memory. In this implementation, each thread block is responsible
for computing one square sub-matrix Csub of C and each thread within the block is
responsible for computing one element of Csub. As illustrated in Figure 10, Csub is equal
to the product of two rectangular matrices: the sub-matrix of A of dimension (A.width,
block_size) that has the same row indices as Csub, and the sub-matrix of B of dimension
(block_size, A.width )that has the same column indices as Csub. In order to fit into the
device's resources, these two rectangular matrices are divided into as many square
matrices of dimension block_size as necessary and Csub is computed as the sum of the
products of these square matrices. Each of these products is performed by first loading
the two corresponding square matrices from global memory to shared memory with one
thread loading one element of each matrix, and then by having each thread compute one
element of the product. Each thread accumulates the result of each of these products into
a register and once done writes the result to global memory.
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By blocking the computation this way, we take advantage of fast shared memory and
save a lot of global memory bandwidth since A is only read (B.width / block_size) times
from global memory and B is read (A.height / block_size) times.
The Matrix type from the previous code sample is augmented with a stride field, so that
sub-matrices can be efficiently represented with the same type. __device__ functions are
used to get and set elements and build any sub-matrix from a matrix.
// Matrices are stored in row-major order:
// M(row, col) = *(M.elements + row * M.stride + col)
typedef struct {
int width;
int height;
int stride;
float* elements;
} Matrix;
// Get a matrix element
__device__ float GetElement(const Matrix A, int row, int col)
{
return A.elements[row * A.stride + col];
}
// Set a matrix element
__device__ void SetElement(Matrix A, int row, int col,
float value)
{
A.elements[row * A.stride + col] = value;
}
// Get the BLOCK_SIZExBLOCK_SIZE sub-matrix Asub of A that is
// located col sub-matrices to the right and row sub-matrices down
// from the upper-left corner of A
__device__ Matrix GetSubMatrix(Matrix A, int row, int col)
{
Matrix Asub;
Asub.width = BLOCK_SIZE;
Asub.height = BLOCK_SIZE;
Asub.stride = A.stride;
Asub.elements = &A.elements[A.stride * BLOCK_SIZE * row
+ BLOCK_SIZE * col];
return Asub;
}
// Thread block size
#define BLOCK_SIZE 16
// Forward declaration of the matrix multiplication kernel
__global__ void MatMulKernel(const Matrix, const Matrix, Matrix);
// Matrix multiplication - Host code
// Matrix dimensions are assumed to be multiples of BLOCK_SIZE
void MatMul(const Matrix A, const Matrix B, Matrix C)
{
// Load A and B to device memory
Matrix d_A;
d_A.width = d_A.stride = A.width; d_A.height = A.height;
size_t size = A.width * A.height * sizeof(float);
cudaMalloc(&d_A.elements, size);
cudaMemcpy(d_A.elements, A.elements, size,
cudaMemcpyHostToDevice);
Matrix d_B;
d_B.width = d_B.stride = B.width; d_B.height = B.height;
size = B.width * B.height * sizeof(float);
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cudaMalloc(&d_B.elements, size);
cudaMemcpy(d_B.elements, B.elements, size,
cudaMemcpyHostToDevice);
// Allocate C in device memory
Matrix d_C;
d_C.width = d_C.stride = C.width; d_C.height = C.height;
size = C.width * C.height * sizeof(float);
cudaMalloc(&d_C.elements, size);
// Invoke kernel
dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
dim3 dimGrid(B.width / dimBlock.x, A.height / dimBlock.y);
MatMulKernel<<<dimGrid, dimBlock>>>(d_A, d_B, d_C);
// Read C from device memory
cudaMemcpy(C.elements, d_C.elements, size,
cudaMemcpyDeviceToHost);
// Free device memory
cudaFree(d_A.elements);
cudaFree(d_B.elements);
cudaFree(d_C.elements);
}
// Matrix multiplication kernel called by MatMul()
__global__ void MatMulKernel(Matrix A, Matrix B, Matrix C)
{
// Block row and column
int blockRow = blockIdx.y;
int blockCol = blockIdx.x;
// Each thread block computes one sub-matrix Csub of C
Matrix Csub = GetSubMatrix(C, blockRow, blockCol);
// Each thread computes one element of Csub
// by accumulating results into Cvalue
float Cvalue = 0;
// Thread row and column within Csub
int row = threadIdx.y;
int col = threadIdx.x;
// Loop over all the sub-matrices of A and B that are
// required to compute Csub
// Multiply each pair of sub-matrices together
// and accumulate the results
for (int m = 0; m < (A.width / BLOCK_SIZE); ++m) {
// Get sub-matrix Asub of A
Matrix Asub = GetSubMatrix(A, blockRow, m);
// Get sub-matrix Bsub of B
Matrix Bsub = GetSubMatrix(B, m, blockCol);
// Shared memory used to store Asub and Bsub respectively
__shared__ float As[BLOCK_SIZE][BLOCK_SIZE];
__shared__ float Bs[BLOCK_SIZE][BLOCK_SIZE];
// Load Asub and Bsub from device memory to shared memory
// Each thread loads one element of each sub-matrix
As[row][col] = GetElement(Asub, row, col);
Bs[row][col] = GetElement(Bsub, row, col);
// Synchronize to make sure the sub-matrices are loaded
// before starting the computation
__syncthreads();
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// Multiply Asub and Bsub together
for (int e = 0; e < BLOCK_SIZE; ++e)
Cvalue += As[row][e] * Bs[e][col];
// Synchronize to make sure that the preceding
// computation is done before loading two new
// sub-matrices of A and B in the next iteration
__syncthreads();
}
// Write Csub to device memory
// Each thread writes one element
SetElement(Csub, row, col, Cvalue);
}
A
B
C
C
sub
BLOCK_
SI ZE
B. w idt h
A.w idth
BLOCK_SI ZE
BLOCK_SI ZE
BLOCK
_SI ZE
BLOCK_SI ZE
BLOCK_SI ZE
blockRow
row
0
BLOCK_SI ZE
-1
BLOCK_SI ZE-1
0
col
blockCol
A.height
B. height
Figure10 Matrix Multiplication with Shared Memory
3.2.4.Page-Locked Host Memory
The runtime provides functions to allow the use of page-locked (also known as pinned)
host memory (as opposed to regular pageable host memory allocated by malloc()):
‣cudaHostAlloc() and cudaFreeHost() allocate and free page-locked host
memory;
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‣cudaHostRegister() page-locks a range of memory allocated by malloc() (see
reference manual for limitations).
Using page-locked host memory has several benefits:
‣Copies between page-locked host memory and device memory can be performed
concurrently with kernel execution for some devices as mentioned in Asynchronous
Concurrent Execution.
‣On some devices, page-locked host memory can be mapped into the address space
of the device, eliminating the need to copy it to or from device memory as detailed
in Mapped Memory.
‣On systems with a front-side bus, bandwidth between host memory and device
memory is higher if host memory is allocated as page-locked and even higher if
in addition it is allocated as write-combining as described in Write-Combining
Memory.
Page-locked host memory is a scarce resource however, so allocations in page-locked
memory will start failing long before allocations in pageable memory. In addition, by
reducing the amount of physical memory available to the operating system for paging,
consuming too much page-locked memory reduces overall system performance.
The simple zero-copy CUDA sample comes with a detailed document on the page-
locked memory APIs.
3.2.4.1.Portable Memory
A block of page-locked memory can be used in conjunction with any device in the
system (see Multi-Device System for more details on multi-device systems), but by
default, the benefits of using page-locked memory described above are only available in
conjunction with the device that was current when the block was allocated (and with all
devices sharing the same unified address space, if any, as described in Unified Virtual
Address Space). To make these advantages available to all devices, the block needs to be
allocated by passing the flag cudaHostAllocPortable to cudaHostAlloc() or page-
locked by passing the flag cudaHostRegisterPortable to cudaHostRegister().
3.2.4.2.Write-Combining Memory
By default page-locked host memory is allocated as cacheable. It can optionally be
allocated as write-combining instead by passing flag cudaHostAllocWriteCombined
to cudaHostAlloc(). Write-combining memory frees up the host's L1 and L2 cache
resources, making more cache available to the rest of the application. In addition, write-
combining memory is not snooped during transfers across the PCI Express bus, which
can improve transfer performance by up to 40%.
Reading from write-combining memory from the host is prohibitively slow, so write-
combining memory should in general be used for memory that the host only writes to.
3.2.4.3.Mapped Memory
A block of page-locked host memory can also be mapped into the address space
of the device by passing flag cudaHostAllocMapped to cudaHostAlloc() or by
passing flag cudaHostRegisterMapped to cudaHostRegister(). Such a block
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has therefore in general two addresses: one in host memory that is returned by
cudaHostAlloc() or malloc(), and one in device memory that can be retrieved
using cudaHostGetDevicePointer() and then used to access the block from within a
kernel. The only exception is for pointers allocated with cudaHostAlloc() and when a
unified address space is used for the host and the device as mentioned in Unified Virtual
Address Space.
Accessing host memory directly from within a kernel has several advantages:
‣There is no need to allocate a block in device memory and copy data between this
block and the block in host memory; data transfers are implicitly performed as
needed by the kernel;
‣There is no need to use streams (see Concurrent Data Transfers) to overlap data
transfers with kernel execution; the kernel-originated data transfers automatically
overlap with kernel execution.
Since mapped page-locked memory is shared between host and device however,
the application must synchronize memory accesses using streams or events (see
Asynchronous Concurrent Execution) to avoid any potential read-after-write, write-
after-read, or write-after-write hazards.
To be able to retrieve the device pointer to any mapped page-locked memory, page-
locked memory mapping must be enabled by calling cudaSetDeviceFlags() with
the cudaDeviceMapHost flag before any other CUDA call is performed. Otherwise,
cudaHostGetDevicePointer() will return an error.
cudaHostGetDevicePointer() also returns an error if the device does not support
mapped page-locked host memory. Applications may query this capability by checking
the canMapHostMemory device property (see Device Enumeration), which is equal to 1
for devices that support mapped page-locked host memory.
Note that atomic functions (see Atomic Functions) operating on mapped page-locked
memory are not atomic from the point of view of the host or other devices.
Also note that CUDA runtime requires that 1-byte, 2-byte, 4-byte, and 8-byte naturally
aligned loads and stores to host memory initiated from the device are preserved as
single accesses from the point of view of the host and other devices. On some platforms,
atomics to memory may be broken by the hardware into separate load and store
operations. These component load and store operations have the same requirements on
preservation of naturally aligned accesses. As an example, the CUDA runtime does not
support a PCI Express bus topology where a PCI Express bridge splits 8-byte naturally
aligned writes into two 4-byte writes between the device and the host.
3.2.5.Asynchronous Concurrent Execution
CUDA exposes the following operations as independent tasks that can operate
concurrently with one another:
‣Computation on the host;
‣Computation on the device;
‣Memory transfers from the host to the device;
‣Memory transfers from the device to the host;
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‣Memory transfers within the memory of a given device;
‣Memory transfers among devices.
The level of concurrency achieved between these operations will depend on the feature
set and compute capability of the device as described below.
3.2.5.1.Concurrent Execution between Host and Device
Concurrent host execution is facilitated through asynchronous library functions that
return control to the host thread before the device completes the requested task. Using
asynchronous calls, many device operations can be queued up together to be executed
by the CUDA driver when appropriate device resources are available. This relieves the
host thread of much of the responsibility to manage the device, leaving it free for other
tasks. The following device operations are asynchronous with respect to the host:
‣Kernel launches;
‣Memory copies within a single device's memory;
‣Memory copies from host to device of a memory block of 64 KB or less;
‣Memory copies performed by functions that are suffixed with Async;
‣Memory set function calls.
Programmers can globally disable asynchronicity of kernel launches for all CUDA
applications running on a system by setting the CUDA_LAUNCH_BLOCKING environment
variable to 1. This feature is provided for debugging purposes only and should not be
used as a way to make production software run reliably.
Kernel launches are synchronous if hardware counters are collected via a profiler
(Nsight, Visual Profiler) unless concurrent kernel profiling is enabled. Async memory
copies will also be synchronous if they involve host memory that is not page-locked.
3.2.5.2.Concurrent Kernel Execution
Some devices of compute capability 2.x and higher can execute multiple
kernels concurrently. Applications may query this capability by checking the
concurrentKernels device property (see Device Enumeration), which is equal to 1 for
devices that support it.
The maximum number of kernel launches that a device can execute concurrently
depends on its compute capability and is listed in Table 14.
A kernel from one CUDA context cannot execute concurrently with a kernel from
another CUDA context.
Kernels that use many textures or a large amount of local memory are less likely to
execute concurrently with other kernels.
3.2.5.3.Overlap of Data Transfer and Kernel Execution
Some devices can perform an asynchronous memory copy to or from the GPU
concurrently with kernel execution. Applications may query this capability by checking
the asyncEngineCount device property (see Device Enumeration), which is greater
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than zero for devices that support it. If host memory is involved in the copy, it must be
page-locked.
It is also possible to perform an intra-device copy simultaneously with kernel execution
(on devices that support the concurrentKernels device property) and/or with copies
to or from the device (for devices that support the asyncEngineCount property). Intra-
device copies are initiated using the standard memory copy functions with destination
and source addresses residing on the same device.
3.2.5.4.Concurrent Data Transfers
Some devices of compute capability 2.x and higher can overlap copies to and from the
device. Applications may query this capability by checking the asyncEngineCount
device property (see Device Enumeration), which is equal to 2 for devices that support
it. In order to be overlapped, any host memory involved in the transfers must be page-
locked.
3.2.5.5.Streams
Applications manage the concurrent operations described above through streams. A
stream is a sequence of commands (possibly issued by different host threads) that
execute in order. Different streams, on the other hand, may execute their commands out
of order with respect to one another or concurrently; this behavior is not guaranteed and
should therefore not be relied upon for correctness (e.g., inter-kernel communication is
undefined).
3.2.5.5.1.Creation and Destruction
A stream is defined by creating a stream object and specifying it as the stream parameter
to a sequence of kernel launches and host <-> device memory copies. The following
code sample creates two streams and allocates an array hostPtr of float in page-
locked memory.
cudaStream_t stream[2];
for (int i = 0; i < 2; ++i)
cudaStreamCreate(&stream[i]);
float* hostPtr;
cudaMallocHost(&hostPtr, 2 * size);
Each of these streams is defined by the following code sample as a sequence of one
memory copy from host to device, one kernel launch, and one memory copy from device
to host:
for (int i = 0; i < 2; ++i) {
cudaMemcpyAsync(inputDevPtr + i * size, hostPtr + i * size,
size, cudaMemcpyHostToDevice, stream[i]);
MyKernel <<<100, 512, 0, stream[i]>>>
(outputDevPtr + i * size, inputDevPtr + i * size, size);
cudaMemcpyAsync(hostPtr + i * size, outputDevPtr + i * size,
size, cudaMemcpyDeviceToHost, stream[i]);
}
Each stream copies its portion of input array hostPtr to array inputDevPtr in device
memory, processes inputDevPtr on the device by calling MyKernel(), and copies
the result outputDevPtr back to the same portion of hostPtr. Overlapping Behavior
describes how the streams overlap in this example depending on the capability of the
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device. Note that hostPtr must point to page-locked host memory for any overlap to
occur.
Streams are released by calling cudaStreamDestroy().
for (int i = 0; i < 2; ++i)
cudaStreamDestroy(stream[i]);
In case the device is still doing work in the stream when cudaStreamDestroy() is
called, the function will return immediately and the resources associated with the stream
will be released automatically once the device has completed all work in the stream.
3.2.5.5.2.Default Stream
Kernel launches and host <-> device memory copies that do not specify any stream
parameter, or equivalently that set the stream parameter to zero, are issued to the default
stream. They are therefore executed in order.
For code that is compiled using the --default-stream per-thread compilation flag
(or that defines the CUDA_API_PER_THREAD_DEFAULT_STREAM macro before including
CUDA headers (cuda.h and cuda_runtime.h)), the default stream is a regular stream
and each host thread has its own default stream.
For code that is compiled using the --default-stream legacy compilation flag, the
default stream is a special stream called the NULL stream and each device has a single
NULL stream used for all host threads. The NULL stream is special as it causes implicit
synchronization as described in Implicit Synchronization.
For code that is compiled without specifying a --default-stream compilation flag, --
default-stream legacy is assumed as the default.
3.2.5.5.3.Explicit Synchronization
There are various ways to explicitly synchronize streams with each other.
cudaDeviceSynchronize() waits until all preceding commands in all streams of all
host threads have completed.
cudaStreamSynchronize()takes a stream as a parameter and waits until all preceding
commands in the given stream have completed. It can be used to synchronize the host
with a specific stream, allowing other streams to continue executing on the device.
cudaStreamWaitEvent()takes a stream and an event as parameters (see Events for
a description of events)and makes all the commands added to the given stream after
the call to cudaStreamWaitEvent()delay their execution until the given event has
completed. The stream can be 0, in which case all the commands added to any stream
after the call to cudaStreamWaitEvent()wait on the event.
cudaStreamQuery()provides applications with a way to know if all preceding
commands in a stream have completed.
To avoid unnecessary slowdowns, all these synchronization functions are usually best
used for timing purposes or to isolate a launch or memory copy that is failing.
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3.2.5.5.4.Implicit Synchronization
Two commands from different streams cannot run concurrently if any one of the
following operations is issued in-between them by the host thread:
‣a page-locked host memory allocation,
‣a device memory allocation,
‣a device memory set,
‣a memory copy between two addresses to the same device memory,
‣any CUDA command to the NULL stream,
‣a switch between the L1/shared memory configurations described in Compute
Capability 3.x and Compute Capability 7.x.
For devices that support concurrent kernel execution and are of compute capability 3.0
or lower, any operation that requires a dependency check to see if a streamed kernel
launch is complete:
‣Can start executing only when all thread blocks of all prior kernel launches from any
stream in the CUDA context have started executing;
‣Blocks all later kernel launches from any stream in the CUDA context until the
kernel launch being checked is complete.
Operations that require a dependency check include any other commands within the
same stream as the launch being checked and any call to cudaStreamQuery() on that
stream. Therefore, applications should follow these guidelines to improve their potential
for concurrent kernel execution:
‣All independent operations should be issued before dependent operations,
‣Synchronization of any kind should be delayed as long as possible.
3.2.5.5.5.Overlapping Behavior
The amount of execution overlap between two streams depends on the order in which
the commands are issued to each stream and whether or not the device supports
overlap of data transfer and kernel execution (see Overlap of Data Transfer and Kernel
Execution), concurrent kernel execution (see Concurrent Kernel Execution), and/or
concurrent data transfers (see Concurrent Data Transfers).
For example, on devices that do not support concurrent data transfers, the two streams
of the code sample of Creation and Destruction do not overlap at all because the
memory copy from host to device is issued to stream[1] after the memory copy from
device to host is issued to stream[0], so it can only start once the memory copy from
device to host issued to stream[0] has completed. If the code is rewritten the following
way (and assuming the device supports overlap of data transfer and kernel execution)
for (int i = 0; i < 2; ++i)
cudaMemcpyAsync(inputDevPtr + i * size, hostPtr + i * size,
size, cudaMemcpyHostToDevice, stream[i]);
for (int i = 0; i < 2; ++i)
MyKernel<<<100, 512, 0, stream[i]>>>
(outputDevPtr + i * size, inputDevPtr + i * size, size);
for (int i = 0; i < 2; ++i)
cudaMemcpyAsync(hostPtr + i * size, outputDevPtr + i * size,
size, cudaMemcpyDeviceToHost, stream[i]);
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then the memory copy from host to device issued to stream[1] overlaps with the kernel
launch issued to stream[0].
On devices that do support concurrent data transfers, the two streams of the code
sample of Creation and Destruction do overlap: The memory copy from host to device
issued to stream[1] overlaps with the memory copy from device to host issued to
stream[0] and even with the kernel launch issued to stream[0] (assuming the device
supports overlap of data transfer and kernel execution). However, for devices of
compute capability 3.0 or lower, the kernel executions cannot possibly overlap because
the second kernel launch is issued to stream[1] after the memory copy from device
to host is issued to stream[0], so it is blocked until the first kernel launch issued to
stream[0] is complete as per Implicit Synchronization. If the code is rewritten as
above, the kernel executions overlap (assuming the device supports concurrent kernel
execution) since the second kernel launch is issued to stream[1] before the memory copy
from device to host is issued to stream[0]. In that case however, the memory copy from
device to host issued to stream[0] only overlaps with the last thread blocks of the kernel
launch issued to stream[1] as per Implicit Synchronization, which can represent only a
small portion of the total execution time of the kernel.
3.2.5.5.6.Callbacks
The runtime provides a way to insert a callback at any point into a stream via
cudaStreamAddCallback(). A callback is a function that is executed on the host once
all commands issued to the stream before the callback have completed. Callbacks in
stream 0 are executed once all preceding tasks and commands issued in all streams
before the callback have completed.
The following code sample adds the callback function MyCallback to each of two
streams after issuing a host-to-device memory copy, a kernel launch and a device-to-host
memory copy into each stream. The callback will begin execution on the host after each
of the device-to-host memory copies completes.
void CUDART_CB MyCallback(cudaStream_t stream, cudaError_t status, void *data){
printf("Inside callback %d\n", (size_t)data);
}
...
for (size_t i = 0; i < 2; ++i) {
cudaMemcpyAsync(devPtrIn[i], hostPtr[i], size, cudaMemcpyHostToDevice,
stream[i]);
MyKernel<<<100, 512, 0, stream[i]>>>(devPtrOut[i], devPtrIn[i], size);
cudaMemcpyAsync(hostPtr[i], devPtrOut[i], size, cudaMemcpyDeviceToHost,
stream[i]);
cudaStreamAddCallback(stream[i], MyCallback, (void*)i, 0);
}
The commands that are issued in a stream (or all commands issued to any stream if the
callback is issued to stream 0) after a callback do not start executing before the callback
has completed. The last parameter of cudaStreamAddCallback() is reserved for future
use.
A callback must not make CUDA API calls (directly or indirectly), as it might end up
waiting on itself if it makes such a call leading to a deadlock.
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3.2.5.5.7.Stream Priorities
The relative priorities of streams can be specified at creation using
cudaStreamCreateWithPriority(). The range of allowable priorities,
ordered as [ highest priority, lowest priority ] can be obtained using the
cudaDeviceGetStreamPriorityRange() function. At runtime, as blocks in low-
priority schemes finish, waiting blocks in higher-priority streams are scheduled in their
place.
The following code sample obtains the allowable range of priorities for the current
device, and creates streams with the highest and lowest available priorities
// get the range of stream priorities for this device
int priority_high, priority_low;
cudaDeviceGetStreamPriorityRange(&priority_low, &priority_high);
// create streams with highest and lowest available priorities
cudaStream_t st_high, st_low;
cudaStreamCreateWithPriority(&st_high, cudaStreamNonBlocking, priority_high);
cudaStreamCreateWithPriority(&st_low, cudaStreamNonBlocking, priority_low);
3.2.5.6.Events
The runtime also provides a way to closely monitor the device's progress, as well as
perform accurate timing, by letting the application asynchronously record events at
any point in the program and query when these events are completed. An event has
completed when all tasks - or optionally, all commands in a given stream - preceding the
event have completed. Events in stream zero are completed after all preceding tasks and
commands in all streams are completed.
3.2.5.6.1.Creation and Destruction
The following code sample creates two events:
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
They are destroyed this way:
cudaEventDestroy(start);
cudaEventDestroy(stop);
3.2.5.6.2.Elapsed Time
The events created in Creation and Destruction can be used to time the code sample of
Creation and Destruction the following way:
cudaEventRecord(start, 0);
for (int i = 0; i < 2; ++i) {
cudaMemcpyAsync(inputDev + i * size, inputHost + i * size,
size, cudaMemcpyHostToDevice, stream[i]);
MyKernel<<<100, 512, 0, stream[i]>>>
(outputDev + i * size, inputDev + i * size, size);
cudaMemcpyAsync(outputHost + i * size, outputDev + i * size,
size, cudaMemcpyDeviceToHost, stream[i]);
}
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
float elapsedTime;
cudaEventElapsedTime(&elapsedTime, start, stop);
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3.2.5.7.Synchronous Calls
When a synchronous function is called, control is not returned to the host thread before
the device has completed the requested task. Whether the host thread will then yield,
block, or spin can be specified by calling cudaSetDeviceFlags()with some specific
flags (see reference manual for details) before any other CUDA call is performed by the
host thread.
3.2.6.Multi-Device System
3.2.6.1.Device Enumeration
A host system can have multiple devices. The following code sample shows how to
enumerate these devices, query their properties, and determine the number of CUDA-
enabled devices.
int deviceCount;
cudaGetDeviceCount(&deviceCount);
int device;
for (device = 0; device < deviceCount; ++device) {
cudaDeviceProp deviceProp;
cudaGetDeviceProperties(&deviceProp, device);
printf("Device %d has compute capability %d.%d.\n",
device, deviceProp.major, deviceProp.minor);
}
3.2.6.2.Device Selection
A host thread can set the device it operates on at any time by calling cudaSetDevice().
Device memory allocations and kernel launches are made on the currently set device;
streams and events are created in association with the currently set device. If no call to
cudaSetDevice() is made, the current device is device 0.
The following code sample illustrates how setting the current device affects memory
allocation and kernel execution.
size_t size = 1024 * sizeof(float);
cudaSetDevice(0); // Set device 0 as current
float* p0;
cudaMalloc(&p0, size); // Allocate memory on device 0
MyKernel<<<1000, 128>>>(p0); // Launch kernel on device 0
cudaSetDevice(1); // Set device 1 as current
float* p1;
cudaMalloc(&p1, size); // Allocate memory on device 1
MyKernel<<<1000, 128>>>(p1); // Launch kernel on device 1
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3.2.6.3.Stream and Event Behavior
A kernel launch will fail if it is issued to a stream that is not associated to the current
device as illustrated in the following code sample.
cudaSetDevice(0); // Set device 0 as current
cudaStream_t s0;
cudaStreamCreate(&s0); // Create stream s0 on device 0
MyKernel<<<100, 64, 0, s0>>>(); // Launch kernel on device 0 in s0
cudaSetDevice(1); // Set device 1 as current
cudaStream_t s1;
cudaStreamCreate(&s1); // Create stream s1 on device 1
MyKernel<<<100, 64, 0, s1>>>(); // Launch kernel on device 1 in s1
// This kernel launch will fail:
MyKernel<<<100, 64, 0, s0>>>(); // Launch kernel on device 1 in s0
A memory copy will succeed even if it is issued to a stream that is not associated to the
current device.
cudaEventRecord() will fail if the input event and input stream are associated to
different devices.
cudaEventElapsedTime() will fail if the two input events are associated to different
devices.
cudaEventSynchronize() and cudaEventQuery() will succeed even if the input
event is associated to a device that is different from the current device.
cudaStreamWaitEvent() will succeed even if the input stream and input event are
associated to different devices. cudaStreamWaitEvent() can therefore be used to
synchronize multiple devices with each other.
Each device has its own default stream (see Default Stream), so commands issued to
the default stream of a device may execute out of order or concurrently with respect to
commands issued to the default stream of any other device.
3.2.6.4.Peer-to-Peer Memory Access
When the application is run as a 64-bit process, devices of compute capability 2.0
and higher from the Tesla series may address each other's memory (i.e., a kernel
executing on one device can dereference a pointer to the memory of the other
device). This peer-to-peer memory access feature is supported between two devices if
cudaDeviceCanAccessPeer() returns true for these two devices.
Peer-to-peer memory access must be enabled between two devices by calling
cudaDeviceEnablePeerAccess() as illustrated in the following code sample. Each
device can support a system-wide maximum of eight peer connections.
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A unified address space is used for both devices (see Unified Virtual Address Space),
so the same pointer can be used to address memory from both devices as shown in the
code sample below.
cudaSetDevice(0); // Set device 0 as current
float* p0;
size_t size = 1024 * sizeof(float);
cudaMalloc(&p0, size); // Allocate memory on device 0
MyKernel<<<1000, 128>>>(p0); // Launch kernel on device 0
cudaSetDevice(1); // Set device 1 as current
cudaDeviceEnablePeerAccess(0, 0); // Enable peer-to-peer access
// with device 0
// Launch kernel on device 1
// This kernel launch can access memory on device 0 at address p0
MyKernel<<<1000, 128>>>(p0);
3.2.6.5.Peer-to-Peer Memory Copy
Memory copies can be performed between the memories of two different devices.
When a unified address space is used for both devices (see Unified Virtual Address
Space), this is done using the regular memory copy functions mentioned in Device
Memory.
Otherwise, this is done using cudaMemcpyPeer(), cudaMemcpyPeerAsync(),
cudaMemcpy3DPeer(), or cudaMemcpy3DPeerAsync() as illustrated in the following
code sample.
cudaSetDevice(0); // Set device 0 as current
float* p0;
size_t size = 1024 * sizeof(float);
cudaMalloc(&p0, size); // Allocate memory on device 0
cudaSetDevice(1); // Set device 1 as current
float* p1;
cudaMalloc(&p1, size); // Allocate memory on device 1
cudaSetDevice(0); // Set device 0 as current
MyKernel<<<1000, 128>>>(p0); // Launch kernel on device 0
cudaSetDevice(1); // Set device 1 as current
cudaMemcpyPeer(p1, 1, p0, 0, size); // Copy p0 to p1
MyKernel<<<1000, 128>>>(p1); // Launch kernel on device 1
A copy (in the implicit NULL stream) between the memories of two different devices:
‣does not start until all commands previously issued to either device have completed
and
‣runs to completion before any commands (see Asynchronous Concurrent Execution)
issued after the copy to either device can start.
Consistent with the normal behavior of streams, an asynchronous copy between the
memories of two devices may overlap with copies or kernels in another stream.
Note that if peer-to-peer access is enabled between two devices via
cudaDeviceEnablePeerAccess() as described in Peer-to-Peer Memory Access, peer-
to-peer memory copy between these two devices no longer needs to be staged through
the host and is therefore faster.
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3.2.7.Unified Virtual Address Space
When the application is run as a 64-bit process, a single address space is used for
the host and all the devices of compute capability 2.0 and higher. All host memory
allocations made via CUDA API calls and all device memory allocations on supported
devices are within this virtual address range. As a consequence:
‣The location of any memory on the host allocated through CUDA, or on any of the
devices which use the unified address space, can be determined from the value of
the pointer using cudaPointerGetAttributes().
‣When copying to or from the memory of any device which uses the unified
address space, the cudaMemcpyKind parameter of cudaMemcpy*() can be set to
cudaMemcpyDefault to determine locations from the pointers. This also works
for host pointers not allocated through CUDA, as long as the current device uses
unified addressing.
‣Allocations via cudaHostAlloc() are automatically portable (see Portable
Memory) across all the devices for which the unified address space is used, and
pointers returned by cudaHostAlloc() can be used directly from within kernels
running on these devices (i.e., there is no need to obtain a device pointer via
cudaHostGetDevicePointer() as described in Mapped Memory.
Applications may query if the unified address space is used for a particular device by
checking that the unifiedAddressing device property (see Device Enumeration) is
equal to 1.
3.2.8.Interprocess Communication
Any device memory pointer or event handle created by a host thread can be directly
referenced by any other thread within the same process. It is not valid outside this
process however, and therefore cannot be directly referenced by threads belonging to a
different process.
To share device memory pointers and events across processes, an application must
use the Inter Process Communication API, which is described in detail in the reference
manual. The IPC API is only supported for 64-bit processes on Linux and for devices of
compute capability 2.0 and higher.
Using this API, an application can get the IPC handle for a given device memory
pointer using cudaIpcGetMemHandle(), pass it to another process using
standard IPC mechanisms (e.g., interprocess shared memory or files), and use
cudaIpcOpenMemHandle() to retrieve a device pointer from the IPC handle that is a
valid pointer within this other process. Event handles can be shared using similar entry
points.
An example of using the IPC API is where a single master process generates a batch
of input data, making the data available to multiple slave processes without requiring
regeneration or copying.
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3.2.9.Error Checking
All runtime functions return an error code, but for an asynchronous function (see
Asynchronous Concurrent Execution), this error code cannot possibly report any of the
asynchronous errors that could occur on the device since the function returns before the
device has completed the task; the error code only reports errors that occur on the host
prior to executing the task, typically related to parameter validation; if an asynchronous
error occurs, it will be reported by some subsequent unrelated runtime function call.
The only way to check for asynchronous errors just after some asynchronous
function call is therefore to synchronize just after the call by calling
cudaDeviceSynchronize() (or by using any other synchronization mechanisms
described in Asynchronous Concurrent Execution) and checking the error code returned
by cudaDeviceSynchronize().
The runtime maintains an error variable for each host thread that is initialized to
cudaSuccess and is overwritten by the error code every time an error occurs (be it
a parameter validation error or an asynchronous error). cudaPeekAtLastError()
returns this variable. cudaGetLastError() returns this variable and resets it to
cudaSuccess.
Kernel launches do not return any error code, so cudaPeekAtLastError() or
cudaGetLastError() must be called just after the kernel launch to retrieve any
pre-launch errors. To ensure that any error returned by cudaPeekAtLastError()
or cudaGetLastError() does not originate from calls prior to the kernel launch,
one has to make sure that the runtime error variable is set to cudaSuccess just before
the kernel launch, for example, by calling cudaGetLastError() just before the
kernel launch. Kernel launches are asynchronous, so to check for asynchronous
errors, the application must synchronize in-between the kernel launch and the call to
cudaPeekAtLastError() or cudaGetLastError().
Note that cudaErrorNotReady that may be returned by cudaStreamQuery() and
cudaEventQuery() is not considered an error and is therefore not reported by
cudaPeekAtLastError() or cudaGetLastError().
3.2.10.Call Stack
On devices of compute capability 2.x and higher, the size of the call stack can be queried
using cudaDeviceGetLimit() and set using cudaDeviceSetLimit().
When the call stack overflows, the kernel call fails with a stack overflow error if the
application is run via a CUDA debugger (cuda-gdb, Nsight) or an unspecified launch
error, otherwise.
3.2.11.Texture and Surface Memory
CUDA supports a subset of the texturing hardware that the GPU uses for graphics
to access texture and surface memory. Reading data from texture or surface memory
instead of global memory can have several performance benefits as described in Device
Memory Accesses.
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There are two different APIs to access texture and surface memory:
‣The texture reference API that is supported on all devices,
‣The texture object API that is only supported on devices of compute capability 3.x.
The texture reference API has limitations that the texture object API does not have. They
are mentioned in Texture Reference API.
3.2.11.1.Texture Memory
Texture memory is read from kernels using the device functions described in Texture
Functions. The process of reading a texture calling one of these functions is called a
texture fetch. Each texture fetch specifies a parameter called a texture object for the texture
object API or a texture reference for the texture reference API.
The texture object or the texture reference specifies:
‣The texture, which is the piece of texture memory that is fetched. Texture objects are
created at runtime and the texture is specified when creating the texture object as
described in Texture Object API. Texture references are created at compile time and
the texture is specified at runtime by bounding the texture reference to the texture
through runtime functions as described in Texture Reference API; several distinct
texture references might be bound to the same texture or to textures that overlap in
memory. A texture can be any region of linear memory or a CUDA array (described
in CUDA Arrays).
‣Its dimensionality that specifies whether the texture is addressed as a one
dimensional array using one texture coordinate, a two-dimensional array using two
texture coordinates, or a three-dimensional array using three texture coordinates.
Elements of the array are called texels, short for texture elements. The texture width,
height, and depth refer to the size of the array in each dimension. Table 14 lists the
maximum texture width, height, and depth depending on the compute capability of
the device.
‣The type of a texel, which is restricted to the basic integer and single-precision
floating-point types and any of the 1-, 2-, and 4-component vector types defined in
char, short, int, long, longlong, float, double that are derived from the basic integer
and single-precision floating-point types.
‣The read mode, which is equal to cudaReadModeNormalizedFloat or
cudaReadModeElementType. If it is cudaReadModeNormalizedFloat and the
type of the texel is a 16-bit or 8-bit integer type, the value returned by the texture
fetch is actually returned as floating-point type and the full range of the integer type
is mapped to [0.0, 1.0] for unsigned integer type and [-1.0, 1.0] for signed integer
type; for example, an unsigned 8-bit texture element with the value 0xff reads as 1. If
it is cudaReadModeElementType, no conversion is performed.
‣Whether texture coordinates are normalized or not. By default, textures
are referenced (by the functions of Texture Functions) using floating-point
coordinates in the range [0, N-1] where N is the size of the texture in the dimension
corresponding to the coordinate. For example, a texture that is 64x32 in size will
be referenced with coordinates in the range [0, 63] and [0, 31] for the x and y
dimensions, respectively. Normalized texture coordinates cause the coordinates
to be specified in the range [0.0, 1.0-1/N] instead of [0, N-1], so the same 64x32
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texture would be addressed by normalized coordinates in the range [0, 1-1/N] in
both the x and y dimensions. Normalized texture coordinates are a natural fit to
some applications' requirements, if it is preferable for the texture coordinates to be
independent of the texture size.
‣The addressing mode. It is valid to call the device functions of Section B.8 with
coordinates that are out of range. The addressing mode defines what happens
in that case. The default addressing mode is to clamp the coordinates to the
valid range: [0, N) for non-normalized coordinates and [0.0, 1.0) for normalized
coordinates. If the border mode is specified instead, texture fetches with out-
of-range texture coordinates return zero. For normalized coordinates, the wrap
mode and the mirror mode are also available. When using the wrap mode, each
coordinate x is converted to frac(x)=x floor(x) where floor(x) is the largest integer
not greater than x. When using the mirror mode, each coordinate x is converted
to frac(x) if floor(x) is even and 1-frac(x) if floor(x) is odd. The addressing mode is
specified as an array of size three whose first, second, and third elements specify the
addressing mode for the first, second, and third texture coordinates, respectively;
the addressing mode are cudaAddressModeBorder, cudaAddressModeClamp,
cudaAddressModeWrap, and cudaAddressModeMirror; cudaAddressModeWrap
and cudaAddressModeMirror are only supported for normalized texture
coordinates
‣The filtering mode which specifies how the value returned when fetching the texture
is computed based on the input texture coordinates. Linear texture filtering may be
done only for textures that are configured to return floating-point data. It performs
low-precision interpolation between neighboring texels. When enabled, the texels
surrounding a texture fetch location are read and the return value of the texture
fetch is interpolated based on where the texture coordinates fell between the texels.
Simple linear interpolation is performed for one-dimensional textures, bilinear
interpolation for two-dimensional textures, and trilinear interpolation for three-
dimensional textures. Texture Fetching gives more details on texture fetching. The
filtering mode is equal to cudaFilterModePoint or cudaFilterModeLinear. If it
is cudaFilterModePoint, the returned value is the texel whose texture coordinates
are the closest to the input texture coordinates. If it is cudaFilterModeLinear, the
returned value is the linear interpolation of the two (for a one-dimensional texture),
four (for a two dimensional texture), or eight (for a three dimensional texture)
texels whose texture coordinates are the closest to the input texture coordinates.
cudaFilterModeLinear is only valid for returned values of floating-point type.
Texture Object API introduces the texture object API.
Texture Reference API introduces the texture reference API.
16-Bit Floating-Point Textures explains how to deal with 16-bit floating-point textures.
Textures can also be layered as described in Layered Textures.
Cubemap Textures and Cubemap Layered Textures describe a special type of texture,
the cubemap texture.
Texture Gather describes a special texture fetch, texture gather.
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3.2.11.1.1.Texture Object API
A texture object is created using cudaCreateTextureObject() from a resource
description of type struct cudaResourceDesc, which specifies the texture, and from a
texture description defined as such:
struct cudaTextureDesc
{
enum cudaTextureAddressMode addressMode[3];
enum cudaTextureFilterMode filterMode;
enum cudaTextureReadMode readMode;
int sRGB;
int normalizedCoords;
unsigned int maxAnisotropy;
enum cudaTextureFilterMode mipmapFilterMode;
float mipmapLevelBias;
float minMipmapLevelClamp;
float maxMipmapLevelClamp;
};
‣addressMode specifies the addressing mode;
‣filterMode specifies the filter mode;
‣readMode specifies the read mode;
‣normalizedCoords specifies whether texture coordinates are normalized or not;
‣See reference manual for sRGB, maxAnisotropy, mipmapFilterMode,
mipmapLevelBias, minMipmapLevelClamp, and maxMipmapLevelClamp.
The following code sample applies some simple transformation kernel to a texture.
// Simple transformation kernel
__global__ void transformKernel(float* output,
cudaTextureObject_t texObj,
int width, int height,
float theta)
{
// Calculate normalized texture coordinates
unsigned int x = blockIdx.x * blockDim.x + threadIdx.x;
unsigned int y = blockIdx.y * blockDim.y + threadIdx.y;
float u = x / (float)width;
float v = y / (float)height;
// Transform coordinates
u -= 0.5f;
v -= 0.5f;
float tu = u * cosf(theta) - v * sinf(theta) + 0.5f;
float tv = v * cosf(theta) + u * sinf(theta) + 0.5f;
// Read from texture and write to global memory
output[y * width + x] = tex2D<float>(texObj, tu, tv);
}
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// Host code
int main()
{
// Allocate CUDA array in device memory
cudaChannelFormatDesc channelDesc =
cudaCreateChannelDesc(32, 0, 0, 0,
cudaChannelFormatKindFloat);
cudaArray* cuArray;
cudaMallocArray(&cuArray, &channelDesc, width, height);
// Copy to device memory some data located at address h_data
// in host memory
cudaMemcpyToArray(cuArray, 0, 0, h_data, size,
cudaMemcpyHostToDevice);
// Specify texture
struct cudaResourceDesc resDesc;
memset(&resDesc, 0, sizeof(resDesc));
resDesc.resType = cudaResourceTypeArray;
resDesc.res.array.array = cuArray;
// Specify texture object parameters
struct cudaTextureDesc texDesc;
memset(&texDesc, 0, sizeof(texDesc));
texDesc.addressMode[0] = cudaAddressModeWrap;
texDesc.addressMode[1] = cudaAddressModeWrap;
texDesc.filterMode = cudaFilterModeLinear;
texDesc.readMode = cudaReadModeElementType;
texDesc.normalizedCoords = 1;
// Create texture object
cudaTextureObject_t texObj = 0;
cudaCreateTextureObject(&texObj, &resDesc, &texDesc, NULL);
// Allocate result of transformation in device memory
float* output;
cudaMalloc(&output, width * height * sizeof(float));
// Invoke kernel
dim3 dimBlock(16, 16);
dim3 dimGrid((width + dimBlock.x - 1) / dimBlock.x,
(height + dimBlock.y - 1) / dimBlock.y);
transformKernel<<<dimGrid, dimBlock>>>(output,
texObj, width, height,
angle);
// Destroy texture object
cudaDestroyTextureObject(texObj);
// Free device memory
cudaFreeArray(cuArray);
cudaFree(output);
return 0;
}
3.2.11.1.2.Texture Reference API
Some of the attributes of a texture reference are immutable and must be known at
compile time; they are specified when declaring the texture reference. A texture
reference is declared at file scope as a variable of type texture:
texture<DataType, Type, ReadMode> texRef;
where:
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‣DataType specifies the type of the texel;
‣Type specifies the type of the texture reference and is equal to
cudaTextureType1D, cudaTextureType2D, or cudaTextureType3D, for a
one-dimensional, two-dimensional, or three-dimensional texture, respectively,
or cudaTextureType1DLayered or cudaTextureType2DLayered for a one-
dimensional or two-dimensional layered texture respectively; Type is an optional
argument which defaults to cudaTextureType1D;
‣ReadMode specifies the read mode; it is an optional argument which defaults to
cudaReadModeElementType.
A texture reference can only be declared as a static global variable and cannot be passed
as an argument to a function.
The other attributes of a texture reference are mutable and can be changed at runtime
through the host runtime. As explained in the reference manual, the runtime API
has a low-level C-style interface and a high-level C++-style interface. The texture
type is defined in the high-level API as a structure publicly derived from the
textureReference type defined in the low-level API as such:
struct textureReference {
int normalized;
enum cudaTextureFilterMode filterMode;
enum cudaTextureAddressMode addressMode[3];
struct cudaChannelFormatDesc channelDesc;
int sRGB;
unsigned int maxAnisotropy;
enum cudaTextureFilterMode mipmapFilterMode;
float mipmapLevelBias;
float minMipmapLevelClamp;
float maxMipmapLevelClamp;
}
‣normalized specifies whether texture coordinates are normalized or not;
‣filterMode specifies the filtering mode;
‣addressMode specifies the addressing mode;
‣channelDesc describes the format of the texel; it must match the DataType
argument of the texture reference declaration; channelDesc is of the following
type:
struct cudaChannelFormatDesc {
int x, y, z, w;
enum cudaChannelFormatKind f;
};
where x, y, z, and w are equal to the number of bits of each component of the
returned value and f is:
‣cudaChannelFormatKindSigned if these components are of signed integer
type,
‣cudaChannelFormatKindUnsigned if they are of unsigned integer type,
‣cudaChannelFormatKindFloat if they are of floating point type.
‣See reference manual for sRGB, maxAnisotropy, mipmapFilterMode,
mipmapLevelBias, minMipmapLevelClamp, and maxMipmapLevelClamp.
normalized, addressMode, and filterMode may be directly modified in host code.
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Before a kernel can use a texture reference to read from texture memory, the
texture reference must be bound to a texture using cudaBindTexture() or
cudaBindTexture2D() for linear memory, or cudaBindTextureToArray() for CUDA
arrays. cudaUnbindTexture() is used to unbind a texture reference. Once a texture
reference has been unbound, it can be safely rebound to another array, even if kernels
that use the previously bound texture have not completed. It is recommended to allocate
two-dimensional textures in linear memory using cudaMallocPitch() and use the
pitch returned by cudaMallocPitch() as input parameter to cudaBindTexture2D().
The following code samples bind a 2D texture reference to linear memory pointed to by
devPtr:
‣Using the low-level API:
texture<float, cudaTextureType2D,
cudaReadModeElementType> texRef;
textureReference* texRefPtr;
cudaGetTextureReference(&texRefPtr, &texRef);
cudaChannelFormatDesc channelDesc =
cudaCreateChannelDesc<float>();
size_t offset;
cudaBindTexture2D(&offset, texRefPtr, devPtr, &channelDesc,
width, height, pitch);
‣Using the high-level API:
texture<float, cudaTextureType2D,
cudaReadModeElementType> texRef;
cudaChannelFormatDesc channelDesc =
cudaCreateChannelDesc<float>();
size_t offset;
cudaBindTexture2D(&offset, texRef, devPtr, channelDesc,
width, height, pitch);
The following code samples bind a 2D texture reference to a CUDA array cuArray:
‣Using the low-level API:
texture<float, cudaTextureType2D,
cudaReadModeElementType> texRef;
textureReference* texRefPtr;
cudaGetTextureReference(&texRefPtr, &texRef);
cudaChannelFormatDesc channelDesc;
cudaGetChannelDesc(&channelDesc, cuArray);
cudaBindTextureToArray(texRef, cuArray, &channelDesc);
‣Using the high-level API:
texture<float, cudaTextureType2D,
cudaReadModeElementType> texRef;
cudaBindTextureToArray(texRef, cuArray);
The format specified when binding a texture to a texture reference must match the
parameters specified when declaring the texture reference; otherwise, the results of
texture fetches are undefined.
There is a limit to the number of textures that can be bound to a kernel as specified in
Table 14.
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The following code sample applies some simple transformation kernel to a texture.
// 2D float texture
texture<float, cudaTextureType2D, cudaReadModeElementType> texRef;
// Simple transformation kernel
__global__ void transformKernel(float* output,
int width, int height,
float theta)
{
// Calculate normalized texture coordinates
unsigned int x = blockIdx.x * blockDim.x + threadIdx.x;
unsigned int y = blockIdx.y * blockDim.y + threadIdx.y;
float u = x / (float)width;
float v = y / (float)height;
// Transform coordinates
u -= 0.5f;
v -= 0.5f;
float tu = u * cosf(theta) - v * sinf(theta) + 0.5f;
float tv = v * cosf(theta) + u * sinf(theta) + 0.5f;
// Read from texture and write to global memory
output[y * width + x] = tex2D(texRef, tu, tv);
}
// Host code
int main()
{
// Allocate CUDA array in device memory
cudaChannelFormatDesc channelDesc =
cudaCreateChannelDesc(32, 0, 0, 0,
cudaChannelFormatKindFloat);
cudaArray* cuArray;
cudaMallocArray(&cuArray, &channelDesc, width, height);
// Copy to device memory some data located at address h_data
// in host memory
cudaMemcpyToArray(cuArray, 0, 0, h_data, size,
cudaMemcpyHostToDevice);
// Set texture reference parameters
texRef.addressMode[0] = cudaAddressModeWrap;
texRef.addressMode[1] = cudaAddressModeWrap;
texRef.filterMode = cudaFilterModeLinear;
texRef.normalized = true;
// Bind the array to the texture reference
cudaBindTextureToArray(texRef, cuArray, channelDesc);
// Allocate result of transformation in device memory
float* output;
cudaMalloc(&output, width * height * sizeof(float));
// Invoke kernel
dim3 dimBlock(16, 16);
dim3 dimGrid((width + dimBlock.x - 1) / dimBlock.x,
(height + dimBlock.y - 1) / dimBlock.y);
transformKernel<<<dimGrid, dimBlock>>>(output, width, height,
angle);
// Free device memory
cudaFreeArray(cuArray);
cudaFree(output);
return 0;
}
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3.2.11.1.3.16-Bit Floating-Point Textures
The 16-bit floating-point or half format supported by CUDA arrays is the same as the
IEEE 754-2008 binary2 format.
CUDA C does not support a matching data type, but provides intrinsic functions to
convert to and from the 32-bit floating-point format via the unsigned short type:
__float2half_rn(float) and __half2float(unsigned short). These functions
are only supported in device code. Equivalent functions for the host code can be found
in the OpenEXR library, for example.
16-bit floating-point components are promoted to 32 bit float during texture fetching
before any filtering is performed.
A channel description for the 16-bit floating-point format can be created by calling one
of the cudaCreateChannelDescHalf*() functions.
3.2.11.1.4.Layered Textures
A one-dimensional or two-dimensional layered texture (also known as texture array in
Direct3D and array texture in OpenGL) is a texture made up of a sequence of layers, all of
which are regular textures of same dimensionality, size, and data type.
A one-dimensional layered texture is addressed using an integer index and a floating-
point texture coordinate; the index denotes a layer within the sequence and the
coordinate addresses a texel within that layer. A two-dimensional layered texture is
addressed using an integer index and two floating-point texture coordinates; the index
denotes a layer within the sequence and the coordinates address a texel within that layer.
A layered texture can only be a CUDA array by calling cudaMalloc3DArray() with the
cudaArrayLayered flag (and a height of zero for one-dimensional layered texture).
Layered textures are fetched using the device functions described in tex1DLayered(),
tex1DLayered(), tex2DLayered(), and tex2DLayered(). Texture filtering (see Texture
Fetching) is done only within a layer, not across layers.
Layered textures are only supported on devices of compute capability 2.0 and higher.
3.2.11.1.5.Cubemap Textures
A cubemap texture is a special type of two-dimensional layered texture that has six layers
representing the faces of a cube:
‣The width of a layer is equal to its height.
‣The cubemap is addressed using three texture coordinates x, y, and z that are
interpreted as a direction vector emanating from the center of the cube and pointing
to one face of the cube and a texel within the layer corresponding to that face. More
specifically, the face is selected by the coordinate with largest magnitude m and the
corresponding layer is addressed using coordinates (s/m+1)/2 and (t/m+1)/2 where s
and t are defined in Table 1.
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Table1 Cubemap Fetch
face m s t
x > 0 0 x -z -y
|x| > |y| and |x| > |z| x < 0 1 -x z -y
y > 0 2 y x z
|y| > |x| and |y| > |z| y < 0 3 -y x -z
z > 0 4 z x -y
|z| > |x| and |z| > |y| z < 0 5 -z -x -y
A layered texture can only be a CUDA array by calling cudaMalloc3DArray() with the
cudaArrayCubemap flag.
Cubemap textures are fetched using the device function described in texCubemap() and
texCubemap().
Cubemap textures are only supported on devices of compute capability 2.0 and higher.
3.2.11.1.6.Cubemap Layered Textures
A cubemap layered texture is a layered texture whose layers are cubemaps of same
dimension.
A cubemap layered texture is addressed using an integer index and three floating-
point texture coordinates; the index denotes a cubemap within the sequence and the
coordinates address a texel within that cubemap.
A layered texture can only be a CUDA array by calling cudaMalloc3DArray() with the
cudaArrayLayered and cudaArrayCubemap flags.
Cubemap layered textures are fetched using the device function described in
texCubemapLayered() and texCubemapLayered(). Texture filtering (see Texture
Fetching) is done only within a layer, not across layers.
Cubemap layered textures are only supported on devices of compute capability 2.0 and
higher.
3.2.11.1.7.Texture Gather
Texture gather is a special texture fetch that is available for two-dimensional textures
only. It is performed by the tex2Dgather() function, which has the same parameters
as tex2D(), plus an additional comp parameter equal to 0, 1, 2, or 3 (see tex2Dgather()
and tex2Dgather()). It returns four 32-bit numbers that correspond to the value of the
component comp of each of the four texels that would have been used for bilinear
filtering during a regular texture fetch. For example, if these texels are of values
(253, 20, 31, 255), (250, 25, 29, 254), (249, 16, 37, 253), (251, 22, 30, 250), and comp is 2,
tex2Dgather() returns (31, 29, 37, 30).
Note that texture coordinates are computed with only 8 bits of fractional precision.
tex2Dgather() may therefore return unexpected results for cases where tex2D()
would use 1.0 for one of its weights (α or β, see Linear Filtering). For example, with an
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x texture coordinate of 2.49805: xB=x-0.5=1.99805, however the fractional part of xB is
stored in an 8-bit fixed-point format. Since 0.99805 is closer to 256.f/256.f than it is to
255.f/256.f, xB has the value 2. A tex2Dgather() in this case would therefore return
indices 2 and 3 in x, instead of indices 1 and 2.
Texture gather is only supported for CUDA arrays created with the
cudaArrayTextureGather flag and of width and height less than the maximum
specified in Table 14 for texture gather, which is smaller than for regular texture fetch.
Texture gather is only supported on devices of compute capability 2.0 and higher.
3.2.11.2.Surface Memory
For devices of compute capability 2.0 and higher, a CUDA array (described in Cubemap
Surfaces), created with the cudaArraySurfaceLoadStore flag, can be read and written
via a surface object or surface reference using the functions described in Surface Functions.
Table 14 lists the maximum surface width, height, and depth depending on the compute
capability of the device.
3.2.11.2.1.Surface Object API
A surface object is created using cudaCreateSurfaceObject() from a resource
description of type struct cudaResourceDesc.
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The following code sample applies some simple transformation kernel to a texture.
// Simple copy kernel
__global__ void copyKernel(cudaSurfaceObject_t inputSurfObj,
cudaSurfaceObject_t outputSurfObj,
int width, int height)
{
// Calculate surface coordinates
unsigned int x = blockIdx.x * blockDim.x + threadIdx.x;
unsigned int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x < width && y < height) {
uchar4 data;
// Read from input surface
surf2Dread(&data, inputSurfObj, x * 4, y);
// Write to output surface
surf2Dwrite(data, outputSurfObj, x * 4, y);
}
}
// Host code
int main()
{
// Allocate CUDA arrays in device memory
cudaChannelFormatDesc channelDesc =
cudaCreateChannelDesc(8, 8, 8, 8,
cudaChannelFormatKindUnsigned);
cudaArray* cuInputArray;
cudaMallocArray(&cuInputArray, &channelDesc, width, height,
cudaArraySurfaceLoadStore);
cudaArray* cuOutputArray;
cudaMallocArray(&cuOutputArray, &channelDesc, width, height,
cudaArraySurfaceLoadStore);
// Copy to device memory some data located at address h_data
// in host memory
cudaMemcpyToArray(cuInputArray, 0, 0, h_data, size,
cudaMemcpyHostToDevice);
// Specify surface
struct cudaResourceDesc resDesc;
memset(&resDesc, 0, sizeof(resDesc));
resDesc.resType = cudaResourceTypeArray;
// Create the surface objects
resDesc.res.array.array = cuInputArray;
cudaSurfaceObject_t inputSurfObj = 0;
cudaCreateSurfaceObject(&inputSurfObj, &resDesc);
resDesc.res.array.array = cuOutputArray;
cudaSurfaceObject_t outputSurfObj = 0;
cudaCreateSurfaceObject(&outputSurfObj, &resDesc);
// Invoke kernel
dim3 dimBlock(16, 16);
dim3 dimGrid((width + dimBlock.x - 1) / dimBlock.x,
(height + dimBlock.y - 1) / dimBlock.y);
copyKernel<<<dimGrid, dimBlock>>>(inputSurfObj,
outputSurfObj,
width, height);
// Destroy surface objects
cudaDestroySurfaceObject(inputSurfObj);
cudaDestroySurfaceObject(outputSurfObj);
// Free device memory
cudaFreeArray(cuInputArray);
cudaFreeArray(cuOutputArray);
return 0;
}
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3.2.11.2.2.Surface Reference API
A surface reference is declared at file scope as a variable of type surface:
surface<void, Type> surfRef;
where Type specifies the type of the surface reference and is equal to
cudaSurfaceType1D, cudaSurfaceType2D, cudaSurfaceType3D,
cudaSurfaceTypeCubemap, cudaSurfaceType1DLayered,
cudaSurfaceType2DLayered, or cudaSurfaceTypeCubemapLayered; Type is an
optional argument which defaults to cudaSurfaceType1D. A surface reference can only
be declared as a static global variable and cannot be passed as an argument to a function.
Before a kernel can use a surface reference to access a CUDA array, the surface reference
must be bound to the CUDA array using cudaBindSurfaceToArray().
The following code samples bind a surface reference to a CUDA array cuArray:
‣Using the low-level API:
surface<void, cudaSurfaceType2D> surfRef;
surfaceReference* surfRefPtr;
cudaGetSurfaceReference(&surfRefPtr, "surfRef");
cudaChannelFormatDesc channelDesc;
cudaGetChannelDesc(&channelDesc, cuArray);
cudaBindSurfaceToArray(surfRef, cuArray, &channelDesc);
‣Using the high-level API:
surface<void, cudaSurfaceType2D> surfRef;
cudaBindSurfaceToArray(surfRef, cuArray);
A CUDA array must be read and written using surface functions of matching
dimensionality and type and via a surface reference of matching dimensionality;
otherwise, the results of reading and writing the CUDA array are undefined.
Unlike texture memory, surface memory uses byte addressing. This means that
the x-coordinate used to access a texture element via texture functions needs to be
multiplied by the byte size of the element to access the same element via a surface
function. For example, the element at texture coordinate x of a one-dimensional
floating-point CUDA array bound to a texture reference texRef and a surface reference
surfRef is read using tex1d(texRef, x) via texRef, but surf1Dread(surfRef,
4*x) via surfRef. Similarly, the element at texture coordinate x and y of a two-
dimensional floating-point CUDA array bound to a texture reference texRef and a
surface reference surfRef is accessed using tex2d(texRef, x, y) via texRef, but
surf2Dread(surfRef, 4*x, y) via surfRef (the byte offset of the y-coordinate is
internally calculated from the underlying line pitch of the CUDA array).
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The following code sample applies some simple transformation kernel to a texture.
// 2D surfaces
surface<void, 2> inputSurfRef;
surface<void, 2> outputSurfRef;
// Simple copy kernel
__global__ void copyKernel(int width, int height)
{
// Calculate surface coordinates
unsigned int x = blockIdx.x * blockDim.x + threadIdx.x;
unsigned int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x < width && y < height) {
uchar4 data;
// Read from input surface
surf2Dread(&data, inputSurfRef, x * 4, y);
// Write to output surface
surf2Dwrite(data, outputSurfRef, x * 4, y);
}
}
// Host code
int main()
{
// Allocate CUDA arrays in device memory
cudaChannelFormatDesc channelDesc =
cudaCreateChannelDesc(8, 8, 8, 8,
cudaChannelFormatKindUnsigned);
cudaArray* cuInputArray;
cudaMallocArray(&cuInputArray, &channelDesc, width, height,
cudaArraySurfaceLoadStore);
cudaArray* cuOutputArray;
cudaMallocArray(&cuOutputArray, &channelDesc, width, height,
cudaArraySurfaceLoadStore);
// Copy to device memory some data located at address h_data
// in host memory
cudaMemcpyToArray(cuInputArray, 0, 0, h_data, size,
cudaMemcpyHostToDevice);
// Bind the arrays to the surface references
cudaBindSurfaceToArray(inputSurfRef, cuInputArray);
cudaBindSurfaceToArray(outputSurfRef, cuOutputArray);
// Invoke kernel
dim3 dimBlock(16, 16);
dim3 dimGrid((width + dimBlock.x - 1) / dimBlock.x,
(height + dimBlock.y - 1) / dimBlock.y);
copyKernel<<<dimGrid, dimBlock>>>(width, height);
// Free device memory
cudaFreeArray(cuInputArray);
cudaFreeArray(cuOutputArray);
return 0;
}
3.2.11.2.3.Cubemap Surfaces
Cubemap surfaces are accessed usingsurfCubemapread() and surfCubemapwrite()
(surfCubemapread and surfCubemapwrite) as a two-dimensional layered surface,
i.e., using an integer index denoting a face and two floating-point texture coordinates
addressing a texel within the layer corresponding to this face. Faces are ordered as
indicated in Table 1.
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3.2.11.2.4.Cubemap Layered Surfaces
Cubemap layered surfaces are accessed using surfCubemapLayeredread()
and surfCubemapLayeredwrite() (surfCubemapLayeredread() and
surfCubemapLayeredwrite()) as a two-dimensional layered surface, i.e., using an integer
index denoting a face of one of the cubemaps and two floating-point texture coordinates
addressing a texel within the layer corresponding to this face. Faces are ordered as
indicated in Table 1, so index ((2 * 6) + 3), for example, accesses the fourth face of the
third cubemap.
3.2.11.3.CUDA Arrays
CUDA arrays are opaque memory layouts optimized for texture fetching. They are one
dimensional, two dimensional, or three-dimensional and composed of elements, each of
which has 1, 2 or 4 components that may be signed or unsigned 8-, 16-, or 32-bit integers,
16-bit floats, or 32-bit floats. CUDA arrays are only accessible by kernels through texture
fetching as described in Texture Memory or surface reading and writing as described in
Surface Memory.
3.2.11.4.Read/Write Coherency
The texture and surface memory is cached (see Device Memory Accesses) and within
the same kernel call, the cache is not kept coherent with respect to global memory
writes and surface memory writes, so any texture fetch or surface read to an address
that has been written to via a global write or a surface write in the same kernel call
returns undefined data. In other words, a thread can safely read some texture or surface
memory location only if this memory location has been updated by a previous kernel
call or memory copy, but not if it has been previously updated by the same thread or
another thread from the same kernel call.
3.2.12.Graphics Interoperability
Some resources from OpenGL and Direct3D may be mapped into the address space of
CUDA, either to enable CUDA to read data written by OpenGL or Direct3D, or to enable
CUDA to write data for consumption by OpenGL or Direct3D.
A resource must be registered to CUDA before it can be mapped using the
functions mentioned in OpenGL Interoperability and Direct3D Interoperability.
These functions return a pointer to a CUDA graphics resource of type struct
cudaGraphicsResource. Registering a resource is potentially high-overhead and
therefore typically called only once per resource. A CUDA graphics resource is
unregistered using cudaGraphicsUnregisterResource(). Each CUDA context which
intends to use the resource is required to register it separately.
Once a resource is registered to CUDA, it can be mapped and unmapped
as many times as necessary using cudaGraphicsMapResources() and
cudaGraphicsUnmapResources(). cudaGraphicsResourceSetMapFlags() can be
called to specify usage hints (write-only, read-only) that the CUDA driver can use to
optimize resource management.
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A mapped resource can be read from or written to by kernels using the device memory
address returned by cudaGraphicsResourceGetMappedPointer() for buffers and
cudaGraphicsSubResourceGetMappedArray() for CUDA arrays.
Accessing a resource through OpenGL, Direct3D, or another CUDA context while
it is mapped produces undefined results. OpenGL Interoperability and Direct3D
Interoperability give specifics for each graphics API and some code samples. SLI
Interoperability gives specifics for when the system is in SLI mode.
3.2.12.1.OpenGL Interoperability
The OpenGL resources that may be mapped into the address space of CUDA are
OpenGL buffer, texture, and renderbuffer objects.
A buffer object is registered using cudaGraphicsGLRegisterBuffer(). In CUDA,
it appears as a device pointer and can therefore be read and written by kernels or via
cudaMemcpy() calls.
A texture or renderbuffer object is registered using
cudaGraphicsGLRegisterImage(). In CUDA, it appears as a CUDA array. Kernels
can read from the array by binding it to a texture or surface reference. They can also
write to it via the surface write functions if the resource has been registered with
the cudaGraphicsRegisterFlagsSurfaceLoadStore flag. The array can also be
read and written via cudaMemcpy2D() calls. cudaGraphicsGLRegisterImage()
supports all texture formats with 1, 2, or 4 components and an internal type of float
(e.g., GL_RGBA_FLOAT32), normalized integer (e.g., GL_RGBA8, GL_INTENSITY16), and
unnormalized integer (e.g., GL_RGBA8UI) (please note that since unnormalized integer
formats require OpenGL 3.0, they can only be written by shaders, not the fixed function
pipeline).
The OpenGL context whose resources are being shared has to be current to the host
thread making any OpenGL interoperability API calls.
Please note: When an OpenGL texture is made bindless (say for example by requesting
an image or texture handle using the glGetTextureHandle*/glGetImageHandle* APIs)
it cannot be registered with CUDA. The application needs to register the texture for
interop before requesting an image or texture handle.
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The following code sample uses a kernel to dynamically modify a 2D width x height
grid of vertices stored in a vertex buffer object:
GLuint positionsVBO;
struct cudaGraphicsResource* positionsVBO_CUDA;
int main()
{
// Initialize OpenGL and GLUT for device 0
// and make the OpenGL context current
...
glutDisplayFunc(display);
// Explicitly set device 0
cudaSetDevice(0);
// Create buffer object and register it with CUDA
glGenBuffers(1, &positionsVBO);
glBindBuffer(GL_ARRAY_BUFFER, positionsVBO);
unsigned int size = width * height * 4 * sizeof(float);
glBufferData(GL_ARRAY_BUFFER, size, 0, GL_DYNAMIC_DRAW);
glBindBuffer(GL_ARRAY_BUFFER, 0);
cudaGraphicsGLRegisterBuffer(&positionsVBO_CUDA,
positionsVBO,
cudaGraphicsMapFlagsWriteDiscard);
// Launch rendering loop
glutMainLoop();
...
}
void display()
{
// Map buffer object for writing from CUDA
float4* positions;
cudaGraphicsMapResources(1, &positionsVBO_CUDA, 0);
size_t num_bytes;
cudaGraphicsResourceGetMappedPointer((void**)&positions,
&num_bytes,
positionsVBO_CUDA));
// Execute kernel
dim3 dimBlock(16, 16, 1);
dim3 dimGrid(width / dimBlock.x, height / dimBlock.y, 1);
createVertices<<<dimGrid, dimBlock>>>(positions, time,
width, height);
// Unmap buffer object
cudaGraphicsUnmapResources(1, &positionsVBO_CUDA, 0);
// Render from buffer object
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);
glBindBuffer(GL_ARRAY_BUFFER, positionsVBO);
glVertexPointer(4, GL_FLOAT, 0, 0);
glEnableClientState(GL_VERTEX_ARRAY);
glDrawArrays(GL_POINTS, 0, width * height);
glDisableClientState(GL_VERTEX_ARRAY);
// Swap buffers
glutSwapBuffers();
glutPostRedisplay();
}
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void deleteVBO()
{
cudaGraphicsUnregisterResource(positionsVBO_CUDA);
glDeleteBuffers(1, &positionsVBO);
}
__global__ void createVertices(float4* positions, float time,
unsigned int width, unsigned int height)
{
unsigned int x = blockIdx.x * blockDim.x + threadIdx.x;
unsigned int y = blockIdx.y * blockDim.y + threadIdx.y;
// Calculate uv coordinates
float u = x / (float)width;
float v = y / (float)height;
u = u * 2.0f - 1.0f;
v = v * 2.0f - 1.0f;
// calculate simple sine wave pattern
float freq = 4.0f;
float w = sinf(u * freq + time)
* cosf(v * freq + time) * 0.5f;
// Write positions
positions[y * width + x] = make_float4(u, w, v, 1.0f);
}
On Windows and for Quadro GPUs, cudaWGLGetDevice() can be used to retrieve the
CUDA device associated to the handle returned by wglEnumGpusNV(). Quadro GPUs
offer higher performance OpenGL interoperability than GeForce and Tesla GPUs in a
multi-GPU configuration where OpenGL rendering is performed on the Quadro GPU
and CUDA computations are performed on other GPUs in the system.
3.2.12.2.Direct3D Interoperability
Direct3D interoperability is supported for Direct3D 9Ex, Direct3D 10, and Direct3D 11.
A CUDA context may interoperate only with Direct3D devices that
fulfill the following criteria: Direct3D 9Ex devices must be created with
DeviceType set to D3DDEVTYPE_HAL and BehaviorFlags with the
D3DCREATE_HARDWARE_VERTEXPROCESSING flag; Direct3D 10 and Direct3D 11 devices
must be created with DriverType set to D3D_DRIVER_TYPE_HARDWARE.
The Direct3D resources that may be mapped into the address space of
CUDA are Direct3D buffers, textures, and surfaces. These resources
are registered using cudaGraphicsD3D9RegisterResource(),
cudaGraphicsD3D10RegisterResource(), and
cudaGraphicsD3D11RegisterResource().
The following code sample uses a kernel to dynamically modify a 2D width x height
grid of vertices stored in a vertex buffer object.
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3.2.12.2.1.Direct3D 9 Version
IDirect3D9* D3D;
IDirect3DDevice9* device;
struct CUSTOMVERTEX {
FLOAT x, y, z;
DWORD color;
};
IDirect3DVertexBuffer9* positionsVB;
struct cudaGraphicsResource* positionsVB_CUDA;
int main()
{
int dev;
// Initialize Direct3D
D3D = Direct3DCreate9Ex(D3D_SDK_VERSION);
// Get a CUDA-enabled adapter
unsigned int adapter = 0;
for (; adapter < g_pD3D->GetAdapterCount(); adapter++) {
D3DADAPTER_IDENTIFIER9 adapterId;
g_pD3D->GetAdapterIdentifier(adapter, 0, &adapterId);
if (cudaD3D9GetDevice(&dev, adapterId.DeviceName)
== cudaSuccess)
break;
}
// Create device
...
D3D->CreateDeviceEx(adapter, D3DDEVTYPE_HAL, hWnd,
D3DCREATE_HARDWARE_VERTEXPROCESSING,
&params, NULL, &device);
// Use the same device
cudaSetDevice(dev);
// Create vertex buffer and register it with CUDA
unsigned int size = width * height * sizeof(CUSTOMVERTEX);
device->CreateVertexBuffer(size, 0, D3DFVF_CUSTOMVERTEX,
D3DPOOL_DEFAULT, &positionsVB, 0);
cudaGraphicsD3D9RegisterResource(&positionsVB_CUDA,
positionsVB,
cudaGraphicsRegisterFlagsNone);
cudaGraphicsResourceSetMapFlags(positionsVB_CUDA,
cudaGraphicsMapFlagsWriteDiscard);
// Launch rendering loop
while (...) {
...
Render();
...
}
...
}
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void Render()
{
// Map vertex buffer for writing from CUDA
float4* positions;
cudaGraphicsMapResources(1, &positionsVB_CUDA, 0);
size_t num_bytes;
cudaGraphicsResourceGetMappedPointer((void**)&positions,
&num_bytes,
positionsVB_CUDA));
// Execute kernel
dim3 dimBlock(16, 16, 1);
dim3 dimGrid(width / dimBlock.x, height / dimBlock.y, 1);
createVertices<<<dimGrid, dimBlock>>>(positions, time,
width, height);
// Unmap vertex buffer
cudaGraphicsUnmapResources(1, &positionsVB_CUDA, 0);
// Draw and present
...
}
void releaseVB()
{
cudaGraphicsUnregisterResource(positionsVB_CUDA);
positionsVB->Release();
}
__global__ void createVertices(float4* positions, float time,
unsigned int width, unsigned int height)
{
unsigned int x = blockIdx.x * blockDim.x + threadIdx.x;
unsigned int y = blockIdx.y * blockDim.y + threadIdx.y;
// Calculate uv coordinates
float u = x / (float)width;
float v = y / (float)height;
u = u * 2.0f - 1.0f;
v = v * 2.0f - 1.0f;
// Calculate simple sine wave pattern
float freq = 4.0f;
float w = sinf(u * freq + time)
* cosf(v * freq + time) * 0.5f;
// Write positions
positions[y * width + x] =
make_float4(u, w, v, __int_as_float(0xff00ff00));
}
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3.2.12.2.2.Direct3D 10 Version
ID3D10Device* device;
struct CUSTOMVERTEX {
FLOAT x, y, z;
DWORD color;
};
ID3D10Buffer* positionsVB;
struct cudaGraphicsResource* positionsVB_CUDA;
int main()
{
int dev;
// Get a CUDA-enabled adapter
IDXGIFactory* factory;
CreateDXGIFactory(__uuidof(IDXGIFactory), (void**)&factory);
IDXGIAdapter* adapter = 0;
for (unsigned int i = 0; !adapter; ++i) {
if (FAILED(factory->EnumAdapters(i, &adapter))
break;
if (cudaD3D10GetDevice(&dev, adapter) == cudaSuccess)
break;
adapter->Release();
}
factory->Release();
// Create swap chain and device
...
D3D10CreateDeviceAndSwapChain(adapter,
D3D10_DRIVER_TYPE_HARDWARE, 0,
D3D10_CREATE_DEVICE_DEBUG,
D3D10_SDK_VERSION,
&swapChainDesc, &swapChain,
&device);
adapter->Release();
// Use the same device
cudaSetDevice(dev);
// Create vertex buffer and register it with CUDA
unsigned int size = width * height * sizeof(CUSTOMVERTEX);
D3D10_BUFFER_DESC bufferDesc;
bufferDesc.Usage = D3D10_USAGE_DEFAULT;
bufferDesc.ByteWidth = size;
bufferDesc.BindFlags = D3D10_BIND_VERTEX_BUFFER;
bufferDesc.CPUAccessFlags = 0;
bufferDesc.MiscFlags = 0;
device->CreateBuffer(&bufferDesc, 0, &positionsVB);
cudaGraphicsD3D10RegisterResource(&positionsVB_CUDA,
positionsVB,
cudaGraphicsRegisterFlagsNone);
cudaGraphicsResourceSetMapFlags(positionsVB_CUDA,
cudaGraphicsMapFlagsWriteDiscard);
// Launch rendering loop
while (...) {
...
Render();
...
}
...
}
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void Render()
{
// Map vertex buffer for writing from CUDA
float4* positions;
cudaGraphicsMapResources(1, &positionsVB_CUDA, 0);
size_t num_bytes;
cudaGraphicsResourceGetMappedPointer((void**)&positions,
&num_bytes,
positionsVB_CUDA));
// Execute kernel
dim3 dimBlock(16, 16, 1);
dim3 dimGrid(width / dimBlock.x, height / dimBlock.y, 1);
createVertices<<<dimGrid, dimBlock>>>(positions, time,
width, height);
// Unmap vertex buffer
cudaGraphicsUnmapResources(1, &positionsVB_CUDA, 0);
// Draw and present
...
}
void releaseVB()
{
cudaGraphicsUnregisterResource(positionsVB_CUDA);
positionsVB->Release();
}
__global__ void createVertices(float4* positions, float time,
unsigned int width, unsigned int height)
{
unsigned int x = blockIdx.x * blockDim.x + threadIdx.x;
unsigned int y = blockIdx.y * blockDim.y + threadIdx.y;
// Calculate uv coordinates
float u = x / (float)width;
float v = y / (float)height;
u = u * 2.0f - 1.0f;
v = v * 2.0f - 1.0f;
// Calculate simple sine wave pattern
float freq = 4.0f;
float w = sinf(u * freq + time)
* cosf(v * freq + time) * 0.5f;
// Write positions
positions[y * width + x] =
make_float4(u, w, v, __int_as_float(0xff00ff00));
}
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3.2.12.2.3.Direct3D 11 Version
ID3D11Device* device;
struct CUSTOMVERTEX {
FLOAT x, y, z;
DWORD color;
};
ID3D11Buffer* positionsVB;
struct cudaGraphicsResource* positionsVB_CUDA;
int main()
{
int dev;
// Get a CUDA-enabled adapter
IDXGIFactory* factory;
CreateDXGIFactory(__uuidof(IDXGIFactory), (void**)&factory);
IDXGIAdapter* adapter = 0;
for (unsigned int i = 0; !adapter; ++i) {
if (FAILED(factory->EnumAdapters(i, &adapter))
break;
if (cudaD3D11GetDevice(&dev, adapter) == cudaSuccess)
break;
adapter->Release();
}
factory->Release();
// Create swap chain and device
...
sFnPtr_D3D11CreateDeviceAndSwapChain(adapter,
D3D11_DRIVER_TYPE_HARDWARE,
0,
D3D11_CREATE_DEVICE_DEBUG,
featureLevels, 3,
D3D11_SDK_VERSION,
&swapChainDesc, &swapChain,
&device,
&featureLevel,
&deviceContext);
adapter->Release();
// Use the same device
cudaSetDevice(dev);
// Create vertex buffer and register it with CUDA
unsigned int size = width * height * sizeof(CUSTOMVERTEX);
D3D11_BUFFER_DESC bufferDesc;
bufferDesc.Usage = D3D11_USAGE_DEFAULT;
bufferDesc.ByteWidth = size;
bufferDesc.BindFlags = D3D11_BIND_VERTEX_BUFFER;
bufferDesc.CPUAccessFlags = 0;
bufferDesc.MiscFlags = 0;
device->CreateBuffer(&bufferDesc, 0, &positionsVB);
cudaGraphicsD3D11RegisterResource(&positionsVB_CUDA,
positionsVB,
cudaGraphicsRegisterFlagsNone);
cudaGraphicsResourceSetMapFlags(positionsVB_CUDA,
cudaGraphicsMapFlagsWriteDiscard);
// Launch rendering loop
while (...) {
...
Render();
...
}
...
}
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void Render()
{
// Map vertex buffer for writing from CUDA
float4* positions;
cudaGraphicsMapResources(1, &positionsVB_CUDA, 0);
size_t num_bytes;
cudaGraphicsResourceGetMappedPointer((void**)&positions,
&num_bytes,
positionsVB_CUDA));
// Execute kernel
dim3 dimBlock(16, 16, 1);
dim3 dimGrid(width / dimBlock.x, height / dimBlock.y, 1);
createVertices<<<dimGrid, dimBlock>>>(positions, time,
width, height);
// Unmap vertex buffer
cudaGraphicsUnmapResources(1, &positionsVB_CUDA, 0);
// Draw and present
...
}
void releaseVB()
{
cudaGraphicsUnregisterResource(positionsVB_CUDA);
positionsVB->Release();
}
__global__ void createVertices(float4* positions, float time,
unsigned int width, unsigned int height)
{
unsigned int x = blockIdx.x * blockDim.x + threadIdx.x;
unsigned int y = blockIdx.y * blockDim.y + threadIdx.y;
// Calculate uv coordinates
float u = x / (float)width;
float v = y / (float)height;
u = u * 2.0f - 1.0f;
v = v * 2.0f - 1.0f;
// Calculate simple sine wave pattern
float freq = 4.0f;
float w = sinf(u * freq + time)
* cosf(v * freq + time) * 0.5f;
// Write positions
positions[y * width + x] =
make_float4(u, w, v, __int_as_float(0xff00ff00));
}
3.2.12.3.SLI Interoperability
In a system with multiple GPUs, all CUDA-enabled GPUs are accessible via the CUDA
driver and runtime as separate devices. There are however special considerations as
described below when the system is in SLI mode.
First, an allocation in one CUDA device on one GPU will consume memory on other
GPUs that are part of the SLI configuration of the Direct3D or OpenGL device. Because
of this, allocations may fail earlier than otherwise expected.
Second, applications should create multiple CUDA contexts, one for each GPU in the SLI
configuration. While this is not a strict requirement, it avoids unnecessary data transfers
between devices. The application can use the cudaD3D[9|10|11]GetDevices() for
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Direct3D and cudaGLGetDevices() for OpenGL set of calls to identify the CUDA
device handle(s) for the device(s) that are performing the rendering in the current
and next frame. Given this information the application will typically choose the
appropriate device and map Direct3D or OpenGL resources to the CUDA device
returned by cudaD3D[9|10|11]GetDevices() or cudaGLGetDevices() when the
deviceList parameter is set to cudaD3D[9|10|11]DeviceListCurrentFrame or
cudaGLDeviceListCurrentFrame.
Please note that resource returned from cudaGraphicsD9D[9|10|
11]RegisterResource and cudaGraphicsGLRegister[Buffer|Image] must be
only used on device the registration happened. Therefore on SLI configurations when
data for different frames is computed on different CUDA devices it is necessary to
register the resources for each separatly.
See Direct3D Interoperability and OpenGL Interoperability for details on how the
CUDA runtime interoperate with Direct3D and OpenGL, respectively.
3.3.Versioning and Compatibility
There are two version numbers that developers should care about when developing a
CUDA application: The compute capability that describes the general specifications and
features of the compute device (see Compute Capability) and the version of the CUDA
driver API that describes the features supported by the driver API and runtime.
The version of the driver API is defined in the driver header file as CUDA_VERSION. It
allows developers to check whether their application requires a newer device driver
than the one currently installed. This is important, because the driver API is backward
compatible, meaning that applications, plug-ins, and libraries (including the C runtime)
compiled against a particular version of the driver API will continue to work on
subsequent device driver releases as illustrated in Figure 11. The driver API is not
forward compatible, which means that applications, plug-ins, and libraries (including the
C runtime) compiled against a particular version of the driver API will not work on
previous versions of the device driver.
It is important to note that there are limitations on the mixing and matching of versions
that is supported:
‣Since only one version of the CUDA Driver can be installed at a time on a system,
the installed driver must be of the same or higher version than the maximum Driver
API version against which any application, plug-ins, or libraries that must run on
that system were built.
‣All plug-ins and libraries used by an application must use the same version of the
CUDA Runtime unless they statically link to the Runtime, in which case multiple
versions of the runtime can coexist in the same process space. Note that if nvcc is
used to link the application, the static version of the CUDA Runtime library will
be used by default, and all CUDA Toolkit libraries are statically linked against the
CUDA Runtime.
‣All plug-ins and libraries used by an application must use the same version of any
libraries that use the runtime (such as cuFFT, cuBLAS, ...) unless statically linking to
those libraries.
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Apps,
Libs &
Plug-ins
Apps,
Libs &
Plug-ins
Apps,
Libs &
Plug-ins
1.0
Driver
Compatible Incompatible
...
...
2.0
Driver
1.1
Driver
Figure11 The Driver API Is Backward but Not Forward Compatible
3.4.Compute Modes
On Tesla solutions running Windows Server 2008 and later or Linux, one can set
any device in a system in one of the three following modes using NVIDIA's System
Management Interface (nvidia-smi), which is a tool distributed as part of the driver:
‣Default compute mode: Multiple host threads can use the device (by calling
cudaSetDevice() on this device, when using the runtime API, or by making
current a context associated to the device, when using the driver API) at the same
time.
‣Exclusive-process compute mode: Only one CUDA context may be created on the
device across all processes in the system and that context may be current to as many
threads as desired within the process that created that context.
‣Exclusive-process-and-thread compute mode: Only one CUDA context may be created
on the device across all processes in the system and that context may only be current
to one thread at a time.
‣Prohibited compute mode: No CUDA context can be created on the device.
This means, in particular, that a host thread using the runtime API without explicitly
calling cudaSetDevice() might be associated with a device other than device 0 if
device 0 turns out to be in the exclusive-process mode and used by another process, or
in the exclusive-process-and-thread mode and used by another thread, or in prohibited
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mode. cudaSetValidDevices() can be used to set a device from a prioritized list of
devices.
Note also that, for devices featuring the Pascal architecture onwards (compute
capability with major revision number 6 and higher), there exists support for
Compute Preemption. This allows compute tasks to be preempted at instruction-
level granularity, rather than thread block granularity as in prior Maxwell and Kepler
GPU architecture, with the benefit that applications with long-running kernels
can be prevented from either monopolizing the system or timing out. However,
there will be context switch overheads associated with Compute Preemption,
which is automatically enabled on those devices for which support exists. The
individual attribute query function cudaDeviceGetAttribute() with the attribute
cudaDevAttrComputePreemptionSupported can be used to determine if the device
in use supports Compute Preemption. Users wishing to avoid context switch overheads
associated with different processes can ensure that only one process is active on the GPU
by selecting exclusive-process mode.
Applications may query the compute mode of a device by checking the computeMode
device property (see Device Enumeration).
3.5.Mode Switches
GPUs that have a display output dedicate some DRAM memory to the so-called primary
surface, which is used to refresh the display device whose output is viewed by the user.
When users initiate a mode switch of the display by changing the resolution or bit depth
of the display (using NVIDIA control panel or the Display control panel on Windows),
the amount of memory needed for the primary surface changes. For example, if the
user changes the display resolution from 1280x1024x32-bit to 1600x1200x32-bit, the
system must dedicate 7.68 MB to the primary surface rather than 5.24 MB. (Full-screen
graphics applications running with anti-aliasing enabled may require much more
display memory for the primary surface.) On Windows, other events that may initiate
display mode switches include launching a full-screen DirectX application, hitting Alt
+Tab to task switch away from a full-screen DirectX application, or hitting Ctrl+Alt+Del
to lock the computer.
If a mode switch increases the amount of memory needed for the primary surface, the
system may have to cannibalize memory allocations dedicated to CUDA applications.
Therefore, a mode switch results in any call to the CUDA runtime to fail and return an
invalid context error.
3.6.Tesla Compute Cluster Mode for Windows
Using NVIDIA's System Management Interface (nvidia-smi), the Windows device driver
can be put in TCC (Tesla Compute Cluster) mode for devices of the Tesla and Quadro
Series of compute capability 2.0 and higher.
This mode has the following primary benefits:
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‣It makes it possible to use these GPUs in cluster nodes with non-NVIDIA integrated
graphics;
‣It makes these GPUs available via Remote Desktop, both directly and via cluster
management systems that rely on Remote Desktop;
‣It makes these GPUs available to applications running as a Windows service (i.e., in
Session 0).
However, the TCC mode removes support for any graphics functionality.
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Chapter4.
HARDWARE IMPLEMENTATION
The NVIDIA GPU architecture is built around a scalable array of multithreaded
Streaming Multiprocessors (SMs). When a CUDA program on the host CPU invokes a
kernel grid, the blocks of the grid are enumerated and distributed to multiprocessors
with available execution capacity. The threads of a thread block execute concurrently
on one multiprocessor, and multiple thread blocks can execute concurrently on one
multiprocessor. As thread blocks terminate, new blocks are launched on the vacated
multiprocessors.
A multiprocessor is designed to execute hundreds of threads concurrently. To manage
such a large amount of threads, it employs a unique architecture called SIMT (Single-
Instruction, Multiple-Thread) that is described in SIMT Architecture. The instructions
are pipelined to leverage instruction-level parallelism within a single thread, as well as
thread-level parallelism extensively through simultaneous hardware multithreading
as detailed in Hardware Multithreading. Unlike CPU cores they are issued in order
however and there is no branch prediction and no speculative execution.
SIMT Architecture and Hardware Multithreading describe the architecture features of
the streaming multiprocessor that are common to all devices. Compute Capability 3.x,
Compute Capability 5.x, Compute Capability 6.x, and Compute Capability 7.x provide
the specifics for devices of compute capabilities 3.x, 5.x, 6.x, and 7.x respectively.
The NVIDIA GPU architecture uses a little-endian representation.
4.1.SIMT Architecture
The multiprocessor creates, manages, schedules, and executes threads in groups of 32
parallel threads called warps. Individual threads composing a warp start together at
the same program address, but they have their own instruction address counter and
register state and are therefore free to branch and execute independently. The term warp
originates from weaving, the first parallel thread technology. A half-warp is either the
first or second half of a warp. A quarter-warp is either the first, second, third, or fourth
quarter of a warp.
When a multiprocessor is given one or more thread blocks to execute, it partitions
them into warps and each warp gets scheduled by a warp scheduler for execution. The
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way a block is partitioned into warps is always the same; each warp contains threads
of consecutive, increasing thread IDs with the first warp containing thread 0. Thread
Hierarchy describes how thread IDs relate to thread indices in the block.
A warp executes one common instruction at a time, so full efficiency is realized when
all 32 threads of a warp agree on their execution path. If threads of a warp diverge via a
data-dependent conditional branch, the warp executes each branch path taken, disabling
threads that are not on that path. Branch divergence occurs only within a warp; different
warps execute independently regardless of whether they are executing common or
disjoint code paths.
The SIMT architecture is akin to SIMD (Single Instruction, Multiple Data) vector
organizations in that a single instruction controls multiple processing elements. A key
difference is that SIMD vector organizations expose the SIMD width to the software,
whereas SIMT instructions specify the execution and branching behavior of a single
thread. In contrast with SIMD vector machines, SIMT enables programmers to write
thread-level parallel code for independent, scalar threads, as well as data-parallel code
for coordinated threads. For the purposes of correctness, the programmer can essentially
ignore the SIMT behavior; however, substantial performance improvements can be
realized by taking care that the code seldom requires threads in a warp to diverge. In
practice, this is analogous to the role of cache lines in traditional code: Cache line size
can be safely ignored when designing for correctness but must be considered in the code
structure when designing for peak performance. Vector architectures, on the other hand,
require the software to coalesce loads into vectors and manage divergence manually.
Prior to Volta, warps used a single program counter shared amongst all 32 threads in the
warp together with an active mask specifying the active threads of the warp. As a result,
threads from the same warp in divergent regions or different states of execution cannot
signal each other or exchange data, and algorithms requiring fine-grained sharing of
data guarded by locks or mutexes can easily lead to deadlock, depending on which warp
the contending threads come from.
Starting with the Volta architecture, Independent Thread Scheduling allows full
concurrency between threads, regardless of warp. With Independent Thread Scheduling,
the GPU maintains execution state per thread, including a program counter and call
stack, and can yield execution at a per-thread granularity, either to make better use of
execution resources or to allow one thread to wait for data to be produced by another.
A schedule optimizer determines how to group active threads from the same warp
together into SIMT units. This retains the high throughput of SIMT execution as in prior
NVIDIA GPUs, but with much more flexibility: threads can now diverge and reconverge
at sub-warp granularity.
Independent Thread Scheduling can lead to a rather different set of threads participating
in the executed code than intended if the developer made assumptions about warp-
synchronicity1 of previous hardware architectures. In particular, any warp-synchronous
code (such as synchronization-free, intra-warp reductions) should be revisited to ensure
compatibility with Volta and beyond. See Compute Capability 7.x for further details.
1The term warp-synchronous refers to code that implicitly assumes threads in the same warp are synchronized at every
instruction.
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Notes
The threads of a warp that are participating in the current instruction are called the
active threads, whereas threads not on the current instruction are inactive (disabled).
Threads can be inactive for a variety of reasons including having exited earlier than
other threads of their warp, having taken a different branch path than the branch path
currently executed by the warp, or being the last threads of a block whose number of
threads is not a multiple of the warp size.
If a non-atomic instruction executed by a warp writes to the same location in global or
shared memory for more than one of the threads of the warp, the number of serialized
writes that occur to that location varies depending on the compute capability of the
device (see Compute Capability 3.x, Compute Capability 5.x, Compute Capability 6.x,
and Compute Capability 7.x), and which thread performs the final write is undefined.
If an atomic instruction executed by a warp reads, modifies, and writes to the same
location in global memory for more than one of the threads of the warp, each read/
modify/write to that location occurs and they are all serialized, but the order in which
they occur is undefined.
4.2.Hardware Multithreading
The execution context (program counters, registers, etc.) for each warp processed by a
multiprocessor is maintained on-chip during the entire lifetime of the warp. Therefore,
switching from one execution context to another has no cost, and at every instruction
issue time, a warp scheduler selects a warp that has threads ready to execute its next
instruction (the active threads of the warp) and issues the instruction to those threads.
In particular, each multiprocessor has a set of 32-bit registers that are partitioned among
the warps, and a parallel data cache or shared memory that is partitioned among the thread
blocks.
The number of blocks and warps that can reside and be processed together on the
multiprocessor for a given kernel depends on the amount of registers and shared
memory used by the kernel and the amount of registers and shared memory available
on the multiprocessor. There are also a maximum number of resident blocks and a
maximum number of resident warps per multiprocessor. These limits as well the amount
of registers and shared memory available on the multiprocessor are a function of the
compute capability of the device and are given in Appendix Compute Capabilities. If
there are not enough registers or shared memory available per multiprocessor to process
at least one block, the kernel will fail to launch.
The total number of warps in a block is as follows:
‣T is the number of threads per block,
‣Wsize is the warp size, which is equal to 32,
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‣ceil(x, y) is equal to x rounded up to the nearest multiple of y.
The total number of registers and total amount of shared memory allocated for a block
are documented in the CUDA Occupancy Calculator provided in the CUDA Toolkit.
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Chapter5.
PERFORMANCE GUIDELINES
5.1.Overall Performance Optimization Strategies
Performance optimization revolves around three basic strategies:
‣Maximize parallel execution to achieve maximum utilization;
‣Optimize memory usage to achieve maximum memory throughput;
‣Optimize instruction usage to achieve maximum instruction throughput.
Which strategies will yield the best performance gain for a particular portion of an
application depends on the performance limiters for that portion; optimizing instruction
usage of a kernel that is mostly limited by memory accesses will not yield any significant
performance gain, for example. Optimization efforts should therefore be constantly
directed by measuring and monitoring the performance limiters, for example using the
CUDA profiler. Also, comparing the floating-point operation throughput or memory
throughput - whichever makes more sense - of a particular kernel to the corresponding
peak theoretical throughput of the device indicates how much room for improvement
there is for the kernel.
5.2.Maximize Utilization
To maximize utilization the application should be structured in a way that it exposes
as much parallelism as possible and efficiently maps this parallelism to the various
components of the system to keep them busy most of the time.
5.2.1.Application Level
At a high level, the application should maximize parallel execution between the host, the
devices, and the bus connecting the host to the devices, by using asynchronous functions
calls and streams as described in Asynchronous Concurrent Execution. It should assign
to each processor the type of work it does best: serial workloads to the host; parallel
workloads to the devices.
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For the parallel workloads, at points in the algorithm where parallelism is broken
because some threads need to synchronize in order to share data with each other,
there are two cases: Either these threads belong to the same block, in which case they
should use __syncthreads() and share data through shared memory within the same
kernel invocation, or they belong to different blocks, in which case they must share
data through global memory using two separate kernel invocations, one for writing to
and one for reading from global memory. The second case is much less optimal since it
adds the overhead of extra kernel invocations and global memory traffic. Its occurrence
should therefore be minimized by mapping the algorithm to the CUDA programming
model in such a way that the computations that require inter-thread communication are
performed within a single thread block as much as possible.
5.2.2.Device Level
At a lower level, the application should maximize parallel execution between the
multiprocessors of a device.
Multiple kernels can execute concurrently on a device, so maximum utilization can
also be achieved by using streams to enable enough kernels to execute concurrently as
described in Asynchronous Concurrent Execution.
5.2.3.Multiprocessor Level
At an even lower level, the application should maximize parallel execution between the
various functional units within a multiprocessor.
As described in Hardware Multithreading, a GPU multiprocessor relies on thread-
level parallelism to maximize utilization of its functional units. Utilization is therefore
directly linked to the number of resident warps. At every instruction issue time, a
warp scheduler selects a warp that is ready to execute its next instruction, if any, and
issues the instruction to the active threads of the warp. The number of clock cycles it
takes for a warp to be ready to execute its next instruction is called the latency, and
full utilization is achieved when all warp schedulers always have some instruction to
issue for some warp at every clock cycle during that latency period, or in other words,
when latency is completely "hidden". The number of instructions required to hide a
latency of L clock cycles depends on the respective throughputs of these instructions
(see Arithmetic Instructions for the throughputs of various arithmetic instructions).
Assuming maximum throughput for all instructions, it is: 8L for devices of compute
capability 3.x since a multiprocessor issues a pair of instructions per warp over one clock
cycle for four warps at a time, as mentioned in Compute Capability 3.x.
For devices of compute capability 3.x, the eight instructions issued every cycle are four
pairs for four different warps, each pair being for the same warp.
The most common reason a warp is not ready to execute its next instruction is that the
instruction's input operands are not available yet.
If all input operands are registers, latency is caused by register dependencies, i.e., some
of the input operands are written by some previous instruction(s) whose execution has
not completed yet. In the case of a back-to-back register dependency (i.e., some input
operand is written by the previous instruction), the latency is equal to the execution
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time of the previous instruction and the warp schedulers must schedule instructions for
different warps during that time. Execution time varies depending on the instruction,
but it is typically about 11 clock cycles for devices of compute capability 3.x, which
translates to 44 warps for devices of compute capability 3.x (assuming that warps
execute instructions with maximum throughput, otherwise fewer warps are needed).
This is also assuming enough instruction-level parallelism so that schedulers are always
able to issue pairs of instructions for each warp.
If some input operand resides in off-chip memory, the latency is much higher: 200 to
400 clock cycles for devices of compute capability 3.x. The number of warps required
to keep the warp schedulers busy during such high latency periods depends on the
kernel code and its degree of instruction-level parallelism. In general, more warps are
required if the ratio of the number of instructions with no off-chip memory operands
(i.e., arithmetic instructions most of the time) to the number of instructions with off-chip
memory operands is low (this ratio is commonly called the arithmetic intensity of the
program). For example, assume this ratio is 30, also assume the latencies are 300 cycles
on devices of compute capability 3.x. Then about 40 warps are required for devices of
compute capability 3.x (with the same assumptions as in the previous paragraph).
Another reason a warp is not ready to execute its next instruction is that it is waiting
at some memory fence (Memory Fence Functions) or synchronization point (Memory
Fence Functions). A synchronization point can force the multiprocessor to idle as
more and more warps wait for other warps in the same block to complete execution of
instructions prior to the synchronization point. Having multiple resident blocks per
multiprocessor can help reduce idling in this case, as warps from different blocks do not
need to wait for each other at synchronization points.
The number of blocks and warps residing on each multiprocessor for a given kernel
call depends on the execution configuration of the call (Execution Configuration), the
memory resources of the multiprocessor, and the resource requirements of the kernel as
described in Hardware Multithreading. Register and shared memory usage are reported
by the compiler when compiling with the -ptxas-options=-v option.
The total amount of shared memory required for a block is equal to the sum of the
amount of statically allocated shared memory and the amount of dynamically allocated
shared memory.
The number of registers used by a kernel can have a significant impact on the number
of resident warps. For example, for devices of compute capability 6.x, if a kernel uses
64 registers and each block has 512 threads and requires very little shared memory,
then two blocks (i.e., 32 warps) can reside on the multiprocessor since they require
2x512x64 registers, which exactly matches the number of registers available on the
multiprocessor. But as soon as the kernel uses one more register, only one block (i.e.,
16 warps) can be resident since two blocks would require 2x512x65 registers, which are
more registers than are available on the multiprocessor. Therefore, the compiler attempts
to minimize register usage while keeping register spilling (see Device Memory Accesses)
and the number of instructions to a minimum. Register usage can be controlled using
the maxrregcount compiler option or launch bounds as described in Launch Bounds.
Each double variable and each long long variable uses two registers.
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The effect of execution configuration on performance for a given kernel call generally
depends on the kernel code. Experimentation is therefore recommended. Applications
can also parameterize execution configurations based on register file size and shared
memory size, which depends on the compute capability of the device, as well as on the
number of multiprocessors and memory bandwidth of the device, all of which can be
queried using the runtime (see reference manual).
The number of threads per block should be chosen as a multiple of the warp size to
avoid wasting computing resources with under-populated warps as much as possible.
5.2.3.1.Occupancy Calculator
Several API functions exist to assist programmers in choosing thread block size based on
register and shared memory requirements.
‣The occupancy calculator API,
cudaOccupancyMaxActiveBlocksPerMultiprocessor, can provide an
occupancy prediction based on the block size and shared memory usage of a kernel.
This function reports occupancy in terms of the number of concurrent thread blocks
per multiprocessor.
‣Note that this value can be converted to other metrics. Multiplying by
the number of warps per block yields the number of concurrent warps
per multiprocessor; further dividing concurrent warps by max warps per
multiprocessor gives the occupancy as a percentage.
‣The occupancy-based launch configurator APIs,
cudaOccupancyMaxPotentialBlockSize and
cudaOccupancyMaxPotentialBlockSizeVariableSMem, heuristically calculate
an execution configuration that achieves the maximum multiprocessor-level
occupancy.
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The following code sample calculates the occupancy of MyKernel. It then reports the
occupancy level with the ratio between concurrent warps versus maximum warps per
multiprocessor.
// Device code
__global__ void MyKernel(int *d, int *a, int *b)
{
int idx = threadIdx.x + blockIdx.x * blockDim.x;
d[idx] = a[idx] * b[idx];
}
// Host code
int main()
{
int numBlocks; // Occupancy in terms of active blocks
int blockSize = 32;
// These variables are used to convert occupancy to warps
int device;
cudaDeviceProp prop;
int activeWarps;
int maxWarps;
cudaGetDevice(&device);
cudaGetDeviceProperties(&prop, device);
cudaOccupancyMaxActiveBlocksPerMultiprocessor(
&numBlocks,
MyKernel,
blockSize,
0);
activeWarps = numBlocks * blockSize / prop.warpSize;
maxWarps = prop.maxThreadsPerMultiProcessor / prop.warpSize;
std::cout << "Occupancy: " << (double)activeWarps / maxWarps * 100 << "%" <<
std::endl;
return 0;
}
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The following code sample configures an occupancy-based kernel launch of MyKernel
according to the user input.
// Device code
__global__ void MyKernel(int *array, int arrayCount)
{
int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < arrayCount) {
array[idx] *= array[idx];
}
}
// Host code
int launchMyKernel(int *array, int arrayCount)
{
int blockSize; // The launch configurator returned block size
int minGridSize; // The minimum grid size needed to achieve the
// maximum occupancy for a full device
// launch
int gridSize; // The actual grid size needed, based on input
// size
cudaOccupancyMaxPotentialBlockSize(
&minGridSize,
&blockSize,
(void*)MyKernel,
0,
arrayCount);
// Round up according to array size
gridSize = (arrayCount + blockSize - 1) / blockSize;
MyKernel<<<gridSize, blockSize>>>(array, arrayCount);
cudaDeviceSynchronize();
// If interested, the occupancy can be calculated with
// cudaOccupancyMaxActiveBlocksPerMultiprocessor
return 0;
}
The CUDA Toolkit also provides a self-documenting, standalone occupancy calculator
and launch configurator implementation in <CUDA_Toolkit_Path>/include/
cuda_occupancy.h for any use cases that cannot depend on the CUDA software stack.
A spreadsheet version of the occupancy calculator is also provided. The spreadsheet
version is particularly useful as a learning tool that visualizes the impact of changes
to the parameters that affect occupancy (block size, registers per thread, and shared
memory per thread).
5.3.Maximize Memory Throughput
The first step in maximizing overall memory throughput for the application is to
minimize data transfers with low bandwidth.
That means minimizing data transfers between the host and the device, as detailed in
Data Transfer between Host and Device, since these have much lower bandwidth than
data transfers between global memory and the device.
That also means minimizing data transfers between global memory and the device
by maximizing use of on-chip memory: shared memory and caches (i.e., L1 cache and
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L2 cache available on devices of compute capability 2.x and higher, texture cache and
constant cache available on all devices).
Shared memory is equivalent to a user-managed cache: The application explicitly
allocates and accesses it. As illustrated in CUDA C Runtime, a typical programming
pattern is to stage data coming from device memory into shared memory; in other
words, to have each thread of a block:
‣Load data from device memory to shared memory,
‣Synchronize with all the other threads of the block so that each thread can safely
read shared memory locations that were populated by different threads,
‣Process the data in shared memory,
‣Synchronize again if necessary to make sure that shared memory has been updated
with the results,
‣Write the results back to device memory.
For some applications (e.g., for which global memory access patterns are data-
dependent), a traditional hardware-managed cache is more appropriate to exploit data
locality. As mentioned in Compute Capability 3.x and Compute Capability 7.x, for
devices of compute capability 3.x and 7.x, the same on-chip memory is used for both L1
and shared memory, and how much of it is dedicated to L1 versus shared memory is
configurable for each kernel call.
The throughput of memory accesses by a kernel can vary by an order of magnitude
depending on access pattern for each type of memory. The next step in maximizing
memory throughput is therefore to organize memory accesses as optimally as possible
based on the optimal memory access patterns described in Device Memory Accesses.
This optimization is especially important for global memory accesses as global memory
bandwidth is low, so non-optimal global memory accesses have a higher impact on
performance.
5.3.1.Data Transfer between Host and Device
Applications should strive to minimize data transfer between the host and the device.
One way to accomplish this is to move more code from the host to the device, even
if that means running kernels with low parallelism computations. Intermediate data
structures may be created in device memory, operated on by the device, and destroyed
without ever being mapped by the host or copied to host memory.
Also, because of the overhead associated with each transfer, batching many small
transfers into a single large transfer always performs better than making each transfer
separately.
On systems with a front-side bus, higher performance for data transfers between host
and device is achieved by using page-locked host memory as described in Page-Locked
Host Memory.
In addition, when using mapped page-locked memory (Mapped Memory), there is
no need to allocate any device memory and explicitly copy data between device and
host memory. Data transfers are implicitly performed each time the kernel accesses the
mapped memory. For maximum performance, these memory accesses must be coalesced
as with accesses to global memory (see Device Memory Accesses). Assuming that they
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are and that the mapped memory is read or written only once, using mapped page-
locked memory instead of explicit copies between device and host memory can be a win
for performance.
On integrated systems where device memory and host memory are physically the same,
any copy between host and device memory is superfluous and mapped page-locked
memory should be used instead. Applications may query a device is integrated by
checking that the integrated device property (see Device Enumeration) is equal to 1.
5.3.2.Device Memory Accesses
An instruction that accesses addressable memory (i.e., global, local, shared, constant,
or texture memory) might need to be re-issued multiple times depending on the
distribution of the memory addresses across the threads within the warp. How the
distribution affects the instruction throughput this way is specific to each type of
memory and described in the following sections. For example, for global memory, as a
general rule, the more scattered the addresses are, the more reduced the throughput is.
Global Memory
Global memory resides in device memory and device memory is accessed via 32-, 64-,
or 128-byte memory transactions. These memory transactions must be naturally aligned:
Only the 32-, 64-, or 128-byte segments of device memory that are aligned to their size
(i.e., whose first address is a multiple of their size) can be read or written by memory
transactions.
When a warp executes an instruction that accesses global memory, it coalesces the
memory accesses of the threads within the warp into one or more of these memory
transactions depending on the size of the word accessed by each thread and the
distribution of the memory addresses across the threads. In general, the more
transactions are necessary, the more unused words are transferred in addition to the
words accessed by the threads, reducing the instruction throughput accordingly. For
example, if a 32-byte memory transaction is generated for each thread's 4-byte access,
throughput is divided by 8.
How many transactions are necessary and how much throughput is ultimately affected
varies with the compute capability of the device. Compute Capability 3.x, Compute
Capability 5.x, Compute Capability 6.x and Compute Capability 7.x give more details on
how global memory accesses are handled for various compute capabilities.
To maximize global memory throughput, it is therefore important to maximize
coalescing by:
‣Following the most optimal access patterns based on Compute Capability 3.x,
Compute Capability 5.x, Compute Capability 6.x and Compute Capability 7.x,
‣Using data types that meet the size and alignment requirement detailed in Device
Memory Accesses,
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‣Padding data in some cases, for example, when accessing a two-dimensional array
as described in Device Memory Accesses.
Size and Alignment Requirement
Global memory instructions support reading or writing words of size equal to 1, 2, 4, 8,
or 16 bytes. Any access (via a variable or a pointer) to data residing in global memory
compiles to a single global memory instruction if and only if the size of the data type
is 1, 2, 4, 8, or 16 bytes and the data is naturally aligned (i.e., its address is a multiple of
that size).
If this size and alignment requirement is not fulfilled, the access compiles to multiple
instructions with interleaved access patterns that prevent these instructions from fully
coalescing. It is therefore recommended to use types that meet this requirement for data
that resides in global memory.
The alignment requirement is automatically fulfilled for the built-in types of char, short,
int, long, longlong, float, double like float2 or float4.
For structures, the size and alignment requirements can be enforced by the compiler
using the alignment specifiers __align__(8) or __align__(16), such as
struct __align__(8) {
float x;
float y;
};
or
struct __align__(16) {
float x;
float y;
float z;
};
Any address of a variable residing in global memory or returned by one of the memory
allocation routines from the driver or runtime API is always aligned to at least 256 bytes.
Reading non-naturally aligned 8-byte or 16-byte words produces incorrect results (off by
a few words), so special care must be taken to maintain alignment of the starting address
of any value or array of values of these types. A typical case where this might be easily
overlooked is when using some custom global memory allocation scheme, whereby the
allocations of multiple arrays (with multiple calls to cudaMalloc() or cuMemAlloc())
is replaced by the allocation of a single large block of memory partitioned into multiple
arrays, in which case the starting address of each array is offset from the block's starting
address.
Two-Dimensional Arrays
A common global memory access pattern is when each thread of index (tx,ty) uses the
following address to access one element of a 2D array of width width, located at address
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BaseAddress of type type* (where type meets the requirement described in Maximize
Utilization):
BaseAddress + width * ty + tx
For these accesses to be fully coalesced, both the width of the thread block and the width
of the array must be a multiple of the warp size.
In particular, this means that an array whose width is not a multiple of this size will be
accessed much more efficiently if it is actually allocated with a width rounded up to the
closest multiple of this size and its rows padded accordingly. The cudaMallocPitch()
and cuMemAllocPitch() functions and associated memory copy functions described in
the reference manual enable programmers to write non-hardware-dependent code to
allocate arrays that conform to these constraints.
Local Memory
Local memory accesses only occur for some automatic variables as mentioned in
Variable Memory Space Specifiers. Automatic variables that the compiler is likely to
place in local memory are:
‣Arrays for which it cannot determine that they are indexed with constant quantities,
‣Large structures or arrays that would consume too much register space,
‣Any variable if the kernel uses more registers than available (this is also known as
register spilling).
Inspection of the PTX assembly code (obtained by compiling with the -ptx or-
keep option) will tell if a variable has been placed in local memory during the first
compilation phases as it will be declared using the .local mnemonic and accessed
using the ld.local and st.local mnemonics. Even if it has not, subsequent
compilation phases might still decide otherwise though if they find it consumes too
much register space for the targeted architecture: Inspection of the cubin object using
cuobjdump will tell if this is the case. Also, the compiler reports total local memory
usage per kernel (lmem) when compiling with the --ptxas-options=-v option. Note
that some mathematical functions have implementation paths that might access local
memory.
The local memory space resides in device memory, so local memory accesses have
same high latency and low bandwidth as global memory accesses and are subject to the
same requirements for memory coalescing as described in Device Memory Accesses.
Local memory is however organized such that consecutive 32-bit words are accessed
by consecutive thread IDs. Accesses are therefore fully coalesced as long as all threads
in a warp access the same relative address (e.g., same index in an array variable, same
member in a structure variable).
On some devices of compute capability 3.x local memory accesses are always cached in
L1 and L2 in the same way as global memory accesses (see Compute Capability 3.x).
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On devices of compute capability 5.x and 6.x, local memory accesses are always cached
in L2 in the same way as global memory accesses (see Compute Capability 5.x and
Compute Capability 6.x).
Shared Memory
Because it is on-chip, shared memory has much higher bandwidth and much lower
latency than local or global memory.
To achieve high bandwidth, shared memory is divided into equally-sized memory
modules, called banks, which can be accessed simultaneously. Any memory read or
write request made of n addresses that fall in n distinct memory banks can therefore be
serviced simultaneously, yielding an overall bandwidth that is n times as high as the
bandwidth of a single module.
However, if two addresses of a memory request fall in the same memory bank, there is a
bank conflict and the access has to be serialized. The hardware splits a memory request
with bank conflicts into as many separate conflict-free requests as necessary, decreasing
throughput by a factor equal to the number of separate memory requests. If the number
of separate memory requests is n, the initial memory request is said to cause n-way bank
conflicts.
To get maximum performance, it is therefore important to understand how memory
addresses map to memory banks in order to schedule the memory requests so as
to minimize bank conflicts. This is described in Compute Capability 3.x, Compute
Capability 5.x, Compute Capability 6.x, and Compute Capability 7.x for devices of
compute capability 3.x, 5.x, 6.x and 7.x, respectively.
Constant Memory
The constant memory space resides in device memory and is cached in the constant
cache.
A request is then split into as many separate requests as there are different memory
addresses in the initial request, decreasing throughput by a factor equal to the number
of separate requests.
The resulting requests are then serviced at the throughput of the constant cache in case
of a cache hit, or at the throughput of device memory otherwise.
Texture and Surface Memory
The texture and surface memory spaces reside in device memory and are cached in
texture cache, so a texture fetch or surface read costs one memory read from device
memory only on a cache miss, otherwise it just costs one read from texture cache. The
texture cache is optimized for 2D spatial locality, so threads of the same warp that read
texture or surface addresses that are close together in 2D will achieve best performance.
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Also, it is designed for streaming fetches with a constant latency; a cache hit reduces
DRAM bandwidth demand but not fetch latency.
Reading device memory through texture or surface fetching present some benefits
that can make it an advantageous alternative to reading device memory from global or
constant memory:
‣If the memory reads do not follow the access patterns that global or constant
memory reads must follow to get good performance, higher bandwidth can be
achieved providing that there is locality in the texture fetches or surface reads;
‣Addressing calculations are performed outside the kernel by dedicated units;
‣Packed data may be broadcast to separate variables in a single operation;
‣8-bit and 16-bit integer input data may be optionally converted to 32 bit floating-
point values in the range [0.0, 1.0] or [-1.0, 1.0] (see Texture Memory).
5.4.Maximize Instruction Throughput
To maximize instruction throughput the application should:
‣Minimize the use of arithmetic instructions with low throughput; this includes
trading precision for speed when it does not affect the end result, such as using
intrinsic instead of regular functions (intrinsic functions are listed in Intrinsic
Functions), single-precision instead of double-precision, or flushing denormalized
numbers to zero;
‣Minimize divergent warps caused by control flow instructions as detailed in Control
Flow Instructions
‣Reduce the number of instructions, for example, by optimizing out synchronization
points whenever possible as described in Synchronization Instruction or by using
restricted pointers as described in __restrict__.
In this section, throughputs are given in number of operations per clock cycle per
multiprocessor. For a warp size of 32, one instruction corresponds to 32 operations,
so if N is the number of operations per clock cycle, the instruction throughput is N/32
instructions per clock cycle.
All throughputs are for one multiprocessor. They must be multiplied by the number of
multiprocessors in the device to get throughput for the whole device.
5.4.1.Arithmetic Instructions
Table 2 gives the throughputs of the arithmetic instructions that are natively supported
in hardware for devices of various compute capabilities.
Table2 Throughput of Native Arithmetic Instructions
(Number of Results per Clock Cycle per Multiprocessor)
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Compute Capability
3.0,
3.2
3.5,
3.7
5.0,
5.2 5.3 6.0 6.1 6.2 7.0
16-bit floating-
point add,
multiply, multiply-
add
N/A N/A N/A 256 128 2 256 128
32-bit floating-
point add,
multiply, multiply-
add
192 192 128 128 64 128 128 64
64-bit floating-
point add,
multiply, multiply-
add
8 6424 4 32 4 4 32
32-bit floating-
point reciprocal,
reciprocal
square root,
base-2 logarithm
(__log2f), base
2 exponential
(exp2f), sine
(__sinf), cosine
(__cosf)
32 32 32 32 16 32 32 16
32-bit integer add,
extended-precision
add, subtract,
extended-precision
subtract
160 160 128 128 64 128 128 64
32-bit integer
multiply, multiply-
add, extended-
precision multiply-
add
32 32 Multiple
instruct.
Multiple
instruct.
Multiple
instruct.
Multiple
instruct.
Multiple
instruct. 643
24-bit integer
multiply
(__[u]mul24)
Multiple
instruct.
Multiple
instruct.
Multiple
instruct.
Multiple
instruct.
Multiple
instruct.
Multiple
instruct.
Multiple
instruct.
Multiple
instruct.
32-bit integer shift 32 64464 64 32 64 64 64
compare,
minimum,
maximum
160 160 64 64 32 64 64 64
32-bit integer bit
reverse, bit field
extract/insert
32 32 64 64 32 64 64 Multiple
Instruct.
32-bit bitwise AND,
OR, XOR 160 160 128 128 64 128 128 64
28 for GeForce GPUs
332 for extended-precision
432 for GeForce GPUs
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Compute Capability
3.0,
3.2
3.5,
3.7
5.0,
5.2 5.3 6.0 6.1 6.2 7.0
count of leading
zeros, most
significant non-sign
bit
32 32 32 32 16 32 32 16
population count 32 32 32 32 16 32 32 16
warp shuffle 32 32 32 32 32 32 32 32
sum of absolute
difference 32 32 64 64 32 64 64 64
SIMD video
instructions
vabsdiff2
160 160 Multiple
instruct.
Multiple
instruct.
Multiple
instruct.
Multiple
instruct.
Multiple
instruct.
Multiple
instruct.
SIMD video
instructions
vabsdiff4
160 160 Multiple
instruct.
Multiple
instruct.
Multiple
instruct.
Multiple
instruct.
Multiple
instruct. 64
All other SIMD
video instructions 32 32 Multiple
instruct.
Multiple
instruct.
Multiple
instruct.
Multiple
instruct.
Multiple
instruct.
Multiple
instruct.
Type conversions
from 8-bit and 16-
bit integer to 32-
bit types
128 128 32 32 16 32 32 16
Type conversions
from and to 64-bit
types
8 3254 4 16 4 4 16
All other type
conversions 32 32 32 32 16 32 32 16
Other instructions and functions are implemented on top of the native instructions.
The implementation may be different for devices of different compute capabilities, and
the number of native instructions after compilation may fluctuate with every compiler
version. For complicated functions, there can be multiple code paths depending on
input. cuobjdump can be used to inspect a particular implementation in a cubin object.
The implementation of some functions are readily available on the CUDA header files
(math_functions.h, device_functions.h, ...).
In general, code compiled with -ftz=true (denormalized numbers are flushed to zero)
tends to have higher performance than code compiled with -ftz=false. Similarly,
code compiled with -prec div=false (less precise division) tends to have higher
performance code than code compiled with -prec div=true, and code compiled
with -prec-sqrt=false (less precise square root) tends to have higher performance
than code compiled with -prec-sqrt=true. The nvcc user manual describes these
compilation flags in more details.
58 for GeForce GPUs
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Single-Precision Floating-Point Division
__fdividef(x, y) (see Intrinsic Functions) provides faster single-precision floating-
point division than the division operator.
Single-Precision Floating-Point Reciprocal Square Root
To preserve IEEE-754 semantics the compiler can optimize 1.0/sqrtf() into rsqrtf()
only when both reciprocal and square root are approximate, (i.e., with -prec-
div=false and -prec-sqrt=false). It is therefore recommended to invoke rsqrtf()
directly where desired.
Single-Precision Floating-Point Square Root
Single-precision floating-point square root is implemented as a reciprocal square root
followed by a reciprocal instead of a reciprocal square root followed by a multiplication
so that it gives correct results for 0 and infinity.
Sine and Cosine
sinf(x), cosf(x), tanf(x), sincosf(x), and corresponding double-precision
instructions are much more expensive and even more so if the argument x is large in
magnitude.
More precisely, the argument reduction code (see Mathematical Functions for
implementation) comprises two code paths referred to as the fast path and the slow
path, respectively.
The fast path is used for arguments sufficiently small in magnitude and essentially
consists of a few multiply-add operations. The slow path is used for arguments large in
magnitude and consists of lengthy computations required to achieve correct results over
the entire argument range.
At present, the argument reduction code for the trigonometric functions selects the fast
path for arguments whose magnitude is less than 105615.0f for the single-precision
functions, and less than 2147483648.0 for the double-precision functions.
As the slow path requires more registers than the fast path, an attempt has been made
to reduce register pressure in the slow path by storing some intermediate variables in
local memory, which may affect performance because of local memory high latency and
bandwidth (see Device Memory Accesses). At present, 28 bytes of local memory are
used by single-precision functions, and 44 bytes are used by double-precision functions.
However, the exact amount is subject to change.
Due to the lengthy computations and use of local memory in the slow path, the
throughput of these trigonometric functions is lower by one order of magnitude when
the slow path reduction is required as opposed to the fast path reduction.
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Integer Arithmetic
Integer division and modulo operation are costly as they compile to up to 20
instructions. They can be replaced with bitwise operations in some cases: If n is a power
of 2, (i/n) is equivalent to (i>>log2(n)) and (i%n) is equivalent to (i&(n-1)); the
compiler will perform these conversions if n is literal.
__brev and __popc map to a single instruction and __brevll and __popcll to a few
instructions.
__[u]mul24 are legacy intrinsic functions that no longer have any reason to be used.
Half Precision Arithmetic
In order to achieve good half precision floating-point add, multiply or multiply-add
throughput it is recommended that the half2 datatype is used. Vector intrinsics
(eg. __hadd2, __hsub2, __hmul2, __hfma2) can then be used to do two operations
in a single instruction. Using half2 in place of two calls using half may also help
performance of other intrinsics, such as warp shuffles.
The intrinsic __halves2half2 is provided to convert two half precision values to the
half2 datatype.
Type Conversion
Sometimes, the compiler must insert conversion instructions, introducing additional
execution cycles. This is the case for:
‣Functions operating on variables of type char or short whose operands generally
need to be converted to int,
‣Double-precision floating-point constants (i.e., those constants defined without
any type suffix) used as input to single-precision floating-point computations (as
mandated by C/C++ standards).
This last case can be avoided by using single-precision floating-point constants, defined
with an f suffix such as 3.141592653589793f, 1.0f, 0.5f.
5.4.2.Control Flow Instructions
Any flow control instruction (if, switch, do, for, while) can significantly impact the
effective instruction throughput by causing threads of the same warp to diverge (i.e., to
follow different execution paths). If this happens, the different executions paths have to
be serialized, increasing the total number of instructions executed for this warp.
To obtain best performance in cases where the control flow depends on the thread
ID, the controlling condition should be written so as to minimize the number of
divergent warps. This is possible because the distribution of the warps across the block
is deterministic as mentioned in SIMT Architecture. A trivial example is when the
controlling condition only depends on (threadIdx / warpSize) where warpSize is
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the warp size. In this case, no warp diverges since the controlling condition is perfectly
aligned with the warps.
Sometimes, the compiler may unroll loops or it may optimize out short if or switch
blocks by using branch predication instead, as detailed below. In these cases, no warp
can ever diverge. The programmer can also control loop unrolling using the #pragma
unroll directive (see #pragma unroll).
When using branch predication none of the instructions whose execution depends on
the controlling condition gets skipped. Instead, each of them is associated with a per-
thread condition code or predicate that is set to true or false based on the controlling
condition and although each of these instructions gets scheduled for execution, only
the instructions with a true predicate are actually executed. Instructions with a false
predicate do not write results, and also do not evaluate addresses or read operands.
5.4.3.Synchronization Instruction
Throughput for __syncthreads() is 128 operations per clock cycle for devices of
compute capability 3.x, 32 operations per clock cycle for devices of compute capability
6.0 and 7.0 and 64 operations per clock cycle for devices of compute capability 5.x, 6.1
and 6.2.
Note that __syncthreads() can impact performance by forcing the multiprocessor to
idle as detailed in Device Memory Accesses.
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AppendixA.
CUDA-ENABLED GPUS
http://developer.nvidia.com/cuda-gpus lists all CUDA-enabled devices with their
compute capability.
The compute capability, number of multiprocessors, clock frequency, total amount of
device memory, and other properties can be queried using the runtime (see reference
manual).
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AppendixB.
C LANGUAGE EXTENSIONS
B.1.Function Execution Space Specifiers
Function execution space specifiers denote whether a function executes on the host or on
the device and whether it is callable from the host or from the device.
B.1.1.__device__
The __device__ execution space specifier declares a function that is:
‣Executed on the device,
‣Callable from the device only.
The __global__ and __device__ execution space specifiers cannot be used together.
B.1.2.__global__
The __global__ exection space specifier declares a function as being a kernel. Such a
function is:
‣Executed on the device,
‣Callable from the host,
‣Callable from the device for devices of compute capability 3.2 or higher (see CUDA
Dynamic Parallelism for more details).
A __global__ function must have void return type, and cannot be a member of a class.
Any call to a __global__ function must specify its execution configuration as described
in Execution Configuration.
A call to a __global__ function is asynchronous, meaning it returns before the device
has completed its execution.
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B.1.3.__host__
The __host__ execution space specifier declares a function that is:
‣Executed on the host,
‣Callable from the host only.
It is equivalent to declare a function with only the __host__ execution space specifier or
to declare it without any of the __host__, __device__, or __global__ execution space
specifier; in either case the function is compiled for the host only.
The __global__ and __host__ execution space specifiers cannot be used together.
The __device__ and __host__ execution space specifiers can be used together
however, in which case the function is compiled for both the host and the device.
The __CUDA_ARCH__ macro introduced in Application Compatibility can be used to
differentiate code paths between host and device:
__host__ __device__ func()
{
#if __CUDA_ARCH__ >= 600
// Device code path for compute capability 6.x
#elif __CUDA_ARCH__ >= 500
// Device code path for compute capability 5.x
#elif __CUDA_ARCH__ >= 300
// Device code path for compute capability 3.x
#elif __CUDA_ARCH__ >= 200
// Device code path for compute capability 2.x
#elif !defined(__CUDA_ARCH__)
// Host code path
#endif
}
B.1.4.__noinline__ and __forceinline__
The compiler inlines any __device__ function when deemed appropriate.
The __noinline__ function qualifier can be used as a hint for the compiler not to inline
the function if possible.
The __forceinline__ function qualifier can be used to force the compiler to inline the
function.
The __noinline__ and __forceinline__ function qualifiers cannot be used together,
and neither function qualifier can be applied to an inline function.
B.2.Variable Memory Space Specifiers
Variable memory space specifiers denote the memory location on the device of a
variable.
An automatic variable declared in device code without any of the __device__,
__shared__ and __constant__ memory space specifiers described in this section
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generally resides in a register. However in some cases the compiler might choose to
place it in local memory, which can have adverse performance consequences as detailed
in Device Memory Accesses.
B.2.1.__device__
The __device__ memory space specifier declares a variable that resides on the device.
At most one of the other memory space specifiers defined in the next two sections may
be used together with __device__ to further denote which memory space the variable
belongs to. If none of them is present, the variable:
‣Resides in global memory space,
‣Has the lifetime of the CUDA context in which it is created,
‣Has a distinct object per device,
‣Is accessible from all the threads within the grid and from the host through
the runtime library (cudaGetSymbolAddress() / cudaGetSymbolSize() /
cudaMemcpyToSymbol() / cudaMemcpyFromSymbol()).
B.2.2.__constant__
The __constant__ memory space specifier, optionally used together with __device__,
declares a variable that:
‣Resides in constant memory space,
‣Has the lifetime of the CUDA context in which it is created,
‣Has a distinct object per device,
‣Is accessible from all the threads within the grid and from the host through
the runtime library (cudaGetSymbolAddress() / cudaGetSymbolSize() /
cudaMemcpyToSymbol() / cudaMemcpyFromSymbol()).
B.2.3.__shared__
The __shared__ memory space specifier, optionally used together with __device__,
declares a variable that:
‣Resides in the shared memory space of a thread block,
‣Has the lifetime of the block,
‣Has a distinct object per block,
‣Is only accessible from all the threads within the block.
When declaring a variable in shared memory as an external array such as
extern __shared__ float shared[];
the size of the array is determined at launch time (see Execution Configuration). All
variables declared in this fashion, start at the same address in memory, so that the layout
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of the variables in the array must be explicitly managed through offsets. For example, if
one wants the equivalent of
short array0[128];
float array1[64];
int array2[256];
in dynamically allocated shared memory, one could declare and initialize the arrays the
following way:
extern __shared__ float array[];
__device__ void func() // __device__ or __global__ function
{
short* array0 = (short*)array;
float* array1 = (float*)&array0[128];
int* array2 = (int*)&array1[64];
}
Note that pointers need to be aligned to the type they point to, so the following code, for
example, does not work since array1 is not aligned to 4 bytes.
extern __shared__ float array[];
__device__ void func() // __device__ or __global__ function
{
short* array0 = (short*)array;
float* array1 = (float*)&array0[127];
}
Alignment requirements for the built-in vector types are listed in Table 3.
B.2.4.__managed__
The __managed__ memory space specifier, optionally used together with __device__,
declares a variable that:
‣Can be referenced from both device and host code, e.g., its address can be taken or it
can be read or written directly from a device or host function.
‣Has the lifetime of an application.
See __managed__ Memory Space Specifier for more details.
B.2.5.__restrict__
nvcc supports restricted pointers via the __restrict__ keyword.
Restricted pointers were introduced in C99 to alleviate the aliasing problem that exists in
C-type languages, and which inhibits all kind of optimization from code re-ordering to
common sub-expression elimination.
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Here is an example subject to the aliasing issue, where use of restricted pointer can help
the compiler to reduce the number of instructions:
void foo(const float* a,
const float* b,
float* c)
{
c[0] = a[0] * b[0];
c[1] = a[0] * b[0];
c[2] = a[0] * b[0] * a[1];
c[3] = a[0] * a[1];
c[4] = a[0] * b[0];
c[5] = b[0];
...
}
In C-type languages, the pointers a, b, and c may be aliased, so any write through c
could modify elements of a or b. This means that to guarantee functional correctness, the
compiler cannot load a[0] and b[0] into registers, multiply them, and store the result
to both c[0] and c[1], because the results would differ from the abstract execution
model if, say, a[0] is really the same location as c[0]. So the compiler cannot take
advantage of the common sub-expression. Likewise, the compiler cannot just reorder the
computation of c[4] into the proximity of the computation of c[0] and c[1] because
the preceding write to c[3] could change the inputs to the computation of c[4].
By making a, b, and c restricted pointers, the programmer asserts to the compiler that
the pointers are in fact not aliased, which in this case means writes through c would
never overwrite elements of a or b. This changes the function prototype as follows:
void foo(const float* __restrict__ a,
const float* __restrict__ b,
float* __restrict__ c);
Note that all pointer arguments need to be made restricted for the compiler optimizer
to derive any benefit. With the __restrict__ keywords added, the compiler can now
reorder and do common sub-expression elimination at will, while retaining functionality
identical with the abstract execution model:
void foo(const float* __restrict__ a,
const float* __restrict__ b,
float* __restrict__ c)
{
float t0 = a[0];
float t1 = b[0];
float t2 = t0 * t2;
float t3 = a[1];
c[0] = t2;
c[1] = t2;
c[4] = t2;
c[2] = t2 * t3;
c[3] = t0 * t3;
c[5] = t1;
...
}
The effects here are a reduced number of memory accesses and reduced number of
computations. This is balanced by an increase in register pressure due to "cached" loads
and common sub-expressions.
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Since register pressure is a critical issue in many CUDA codes, use of restricted pointers
can have negative performance impact on CUDA code, due to reduced occupancy.
B.3.Built-in Vector Types
B.3.1.char, short, int, long, longlong, float, double
These are vector types derived from the basic integer and floating-point types. They
are structures and the 1st, 2nd, 3rd, and 4th components are accessible through the
fields x, y, z, and w, respectively. They all come with a constructor function of the form
make_<type name>; for example,
int2 make_int2(int x, int y);
which creates a vector of type int2 with value(x, y).
The alignment requirements of the vector types are detailed in Table 3.
Table3 Alignment Requirements
Type Alignment
char1, uchar1 1
char2, uchar2 2
char3, uchar3 1
char4, uchar4 4
short1, ushort1 2
short2, ushort2 4
short3, ushort3 2
short4, ushort4 8
int1, uint1 4
int2, uint2 8
int3, uint3 4
int4, uint4 16
long1, ulong1 4 if sizeof(long) is equal to sizeof(int) 8, otherwise
long2, ulong2 8 if sizeof(long) is equal to sizeof(int), 16, otherwise
long3, ulong3 4 if sizeof(long) is equal to sizeof(int), 8, otherwise
long4, ulong4 16
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Type Alignment
longlong1, ulonglong1 8
longlong2, ulonglong2 16
float1 4
float2 8
float3 4
float4 16
double1 8
double2 16
B.3.2.dim3
This type is an integer vector type based on uint3 that is used to specify dimensions.
When defining a variable of type dim3, any component left unspecified is initialized to 1.
B.4.Built-in Variables
Built-in variables specify the grid and block dimensions and the block and thread
indices. They are only valid within functions that are executed on the device.
B.4.1.gridDim
This variable is of type dim3 (see dim3) and contains the dimensions of the grid.
B.4.2.blockIdx
This variable is of type uint3 (see char, short, int, long, longlong, float, double) and
contains the block index within the grid.
B.4.3.blockDim
This variable is of type dim3 (see dim3) and contains the dimensions of the block.
B.4.4.threadIdx
This variable is of type uint3 (see char, short, int, long, longlong, float, double ) and
contains the thread index within the block.
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B.4.5.warpSize
This variable is of type int and contains the warp size in threads (see SIMT Architecture
for the definition of a warp).
B.5.Memory Fence Functions
The CUDA programming model assumes a device with a weakly-ordered memory
model, that is the order in which a CUDA thread writes data to shared memory, global
memory, page-locked host memory, or the memory of a peer device is not necessarily the
order in which the data is observed being written by another CUDA or host thread.
For example, if thread 1 executes writeXY() and thread 2 executes readXY() as
defined in the following code sample
__device__ volatile int X = 1, Y = 2;
__device__ void writeXY()
{
X = 10;
Y = 20;
}
__device__ void readXY()
{
int A = X;
int B = Y;
}
it is possible that B ends up equal to 20 and A equal to 1 for thread 2. In a strongly-
ordered memory model, the only possibilities would be:
‣A equal to 1 and B equal to 2,
‣A equal to 10 and B equal to 2,
‣A equal to 10 and B equal to 20,
Memory fence functions can be used to enforce some ordering on memory accesses. The
memory fence functions differ in the scope in which the orderings are enforced but they
are independent of the accessed memory space (shared memory, global memory, page-
locked host memory, and the memory of a peer device).
void __threadfence_block();
ensures that:
‣All writes to all memory made by the calling thread before the call to
__threadfence_block() are observed by all threads in the block of the calling
thread as occurring before all writes to all memory made by the calling thread after
the call to __threadfence_block();
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‣All reads from all memory made by the calling thread before the call to
__threadfence_block() are ordered before all reads from all memory made by
the calling thread after the call to __threadfence_block().
void __threadfence();
acts as __threadfence_block() for all threads in the block of the calling thread and
also ensures that no writes to all memory made by the calling thread after the call to
__threadfence() are observed by any thread in the device as occurring before any
write to all memory made by the calling thread before the call to __threadfence().
Note that for this ordering guarantee to be true, the observing threads must truly
observe the memory and not cached versions of it; this is ensured by using the
volatile keyword as detailed in Volatile Qualifier.
void __threadfence_system();
acts as __threadfence_block() for all threads in the block of the calling thread and
also ensures that all writes to all memory made by the calling thread before the call to
__threadfence_system() are observed by all threads in the device, host threads,
and all threads in peer devices as occurring before all writes to all memory made by the
calling thread after the call to __threadfence_system().
__threadfence_system() is only supported by devices of compute capability 2.x and
higher.
In the previous code sample, inserting a fence function call between X = 10; and Y
= 20; and between int A = X; and int B = Y; would ensure that for thread 2, A
will always be equal to 10 if B is equal to 20. If thread 1 and 2 belong to the same block,
it is enough to use __threadfence_block(). If thread 1 and 2 do not belong to the
same block, __threadfence() must be used if they are CUDA threads from the same
device and __threadfence_system() must be used if they are CUDA threads from
two different devices.
A common use case is when threads consume some data produced by other threads as
illustrated by the following code sample of a kernel that computes the sum of an array
of N numbers in one call. Each block first sums a subset of the array and stores the result
in global memory. When all blocks are done, the last block done reads each of these
partial sums from global memory and sums them to obtain the final result. In order to
determine which block is finished last, each block atomically increments a counter to
signal that it is done with computing and storing its partial sum (see Atomic Functions
about atomic functions). The last block is the one that receives the counter value equal
to gridDim.x-1. If no fence is placed between storing the partial sum and incrementing
the counter, the counter might increment before the partial sum is stored and therefore,
might reach gridDim.x-1 and let the last block start reading partial sums before they
have been actually updated in memory.
Memory fence functions only affect the ordering of memory operations by a thread;
they do not ensure that these memory operations are visible to other threads (like
__syncthreads() does for threads within a block (see Synchronization Functions)). In
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the code sample below, the visibility of memory operations on the result variable is
ensured by declaring it as volatile (see Volatile Qualifier).
__device__ unsigned int count = 0;
__shared__ bool isLastBlockDone;
__global__ void sum(const float* array, unsigned int N,
volatile float* result)
{
// Each block sums a subset of the input array.
float partialSum = calculatePartialSum(array, N);
if (threadIdx.x == 0) {
// Thread 0 of each block stores the partial sum
// to global memory. The compiler will use
// a store operation that bypasses the L1 cache
// since the "result" variable is declared as
// volatile. This ensures that the threads of
// the last block will read the correct partial
// sums computed by all other blocks.
result[blockIdx.x] = partialSum;
// Thread 0 makes sure that the incrementation
// of the "count" variable is only performed after
// the partial sum has been written to global memory.
__threadfence();
// Thread 0 signals that it is done.
unsigned int value = atomicInc(&count, gridDim.x);
// Thread 0 determines if its block is the last
// block to be done.
isLastBlockDone = (value == (gridDim.x - 1));
}
// Synchronize to make sure that each thread reads
// the correct value of isLastBlockDone.
__syncthreads();
if (isLastBlockDone) {
// The last block sums the partial sums
// stored in result[0 .. gridDim.x-1]
float totalSum = calculateTotalSum(result);
if (threadIdx.x == 0) {
// Thread 0 of last block stores the total sum
// to global memory and resets the count
// varialble, so that the next kernel call
// works properly.
result[0] = totalSum;
count = 0;
}
}
}
B.6.Synchronization Functions
void __syncthreads();
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waits until all threads in the thread block have reached this point and all global and
shared memory accesses made by these threads prior to __syncthreads() are visible
to all threads in the block.
__syncthreads() is used to coordinate communication between the threads of the
same block. When some threads within a block access the same addresses in shared
or global memory, there are potential read-after-write, write-after-read, or write-after-
write hazards for some of these memory accesses. These data hazards can be avoided by
synchronizing threads in-between these accesses.
__syncthreads() is allowed in conditional code but only if the conditional evaluates
identically across the entire thread block, otherwise the code execution is likely to hang
or produce unintended side effects.
Devices of compute capability 2.x and higher support three variations of
__syncthreads() described below.
int __syncthreads_count(int predicate);
is identical to __syncthreads() with the additional feature that it evaluates predicate
for all threads of the block and returns the number of threads for which predicate
evaluates to non-zero.
int __syncthreads_and(int predicate);
is identical to __syncthreads() with the additional feature that it evaluates predicate
for all threads of the block and returns non-zero if and only if predicate evaluates to non-
zero for all of them.
int __syncthreads_or(int predicate);
is identical to __syncthreads() with the additional feature that it evaluates predicate
for all threads of the block and returns non-zero if and only if predicate evaluates to non-
zero for any of them.
void __syncwarp(unsigned mask=0xffffffff);
will cause the executing thread to wait until all warp lanes named in mask have
executed a __syncwarp() (with the same mask) before resuming execution. All non-
exited threads named in mask must execute a corresponding __syncwarp() with the
same mask, or the result is undefined.
Executing __syncwarp() guarantees memory ordering among threads participating in
the barrier. Thus, threads within a warp that wish to communicate via memory can store
to memory, execute __syncwarp(), and then safely read values stored by other threads
in the warp.
For .target sm_6x or below, all threads in mask must execute the same
__syncwarp() in convergence, and the union of all values in mask must be equal to
the active mask. Otherwise, the behavior is undefined.
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B.7.Mathematical Functions
The reference manual lists all C/C++ standard library mathematical functions that are
supported in device code and all intrinsic functions that are only supported in device
code.
Mathematical Functions provides accuracy information for some of these functions
when relevant.
B.8.Texture Functions
Texture objects are described in Texture Object API
Texture references are described in Texture Reference API
Texture fetching is described in Texture Fetching.
B.8.1.Texture Object API
B.8.1.1.tex1Dfetch()
template<class T>
T tex1Dfetch(cudaTextureObject_t texObj, int x);
fetches from the region of linear memory specified by the one-dimensional texture
object texObj using integer texture coordinate x. tex1Dfetch() only works with non-
normalized coordinates, so only the border and clamp addressing modes are supported.
It does not perform any texture filtering. For integer types, it may optionally promote
the integer to single-precision floating point.
B.8.1.2.tex1D()
template<class T>
T tex1D(cudaTextureObject_t texObj, float x);
fetches from the CUDA array specified by the one-dimensional texture object texObj
using texture coordinate x.
B.8.1.3.tex1DLod()
template<class T>
T tex1DLod(cudaTextureObject_t texObj, float x, float level);
fetches from the CUDA array specified by the one-dimensional texture object texObj
using texture coordinate x at the level-of-detail level.
B.8.1.4.tex1DGrad()
template<class T>
T tex1DGrad(cudaTextureObject_t texObj, float x, float dx, float dy);
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fetches from the CUDA array specified by the one-dimensional texture object texObj
using texture coordinate x. The level-of-detail is derived from the X-gradient dx and Y-
gradient dy.
B.8.1.5.tex2D()
template<class T>
T tex2D(cudaTextureObject_t texObj, float x, float y);
fetches from the CUDA array or the region of linear memory specified by the two-
dimensional texture object texObj using texture coordinate (x,y).
B.8.1.6.tex2DLod()
template<class T>
tex2DLod(cudaTextureObject_t texObj, float x, float y, float level);
fetches from the CUDA array or the region of linear memory specified by the two-
dimensional texture object texObj using texture coordinate (x,y) at level-of-detail
level.
B.8.1.7.tex2DGrad()
template<class T>
T tex2DGrad(cudaTextureObject_t texObj, float x, float y,
float2 dx, float2 dy);
fetches from the CUDA array specified by the two-dimensional texture object texObj
using texture coordinate (x,y). The level-of-detail is derived from the dx and dy
gradients.
B.8.1.8.tex3D()
template<class T>
T tex3D(cudaTextureObject_t texObj, float x, float y, float z);
fetches from the CUDA array specified by the three-dimensional texture object texObj
using texture coordinate (x,y,z).
B.8.1.9.tex3DLod()
template<class T>
T tex3DLod(cudaTextureObject_t texObj, float x, float y, float z, float level);
fetches from the CUDA array or the region of linear memory specified by the three-
dimensional texture object texObj using texture coordinate (x,y,z) at level-of-detail
level.
B.8.1.10.tex3DGrad()
template<class T>
T tex3DGrad(cudaTextureObject_t texObj, float x, float y, float z,
float4 dx, float4 dy);
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fetches from the CUDA array specified by the three-dimensional texture object texObj
using texture coordinate (x,y,z) at a level-of-detail derived from the X and Y gradients
dx and dy.
B.8.1.11.tex1DLayered()
template<class T>
T tex1DLayered(cudaTextureObject_t texObj, float x, int layer);
fetches from the CUDA array specified by the one-dimensional texture object texObj
using texture coordinate x and index layer, as described in Layered Textures
B.8.1.12.tex1DLayeredLod()
template<class T>
T tex1DLayeredLod(cudaTextureObject_t texObj, float x, int layer, float level);
fetches from the CUDA array specified by the one-dimensional layered texture at layer
layer using texture coordinate x and level-of-detail level.
B.8.1.13.tex1DLayeredGrad()
template<class T>
T tex1DLayeredGrad(cudaTextureObject_t texObj, float x, int layer,
float dx, float dy);
fetches from the CUDA array specified by the one-dimensional layered texture at layer
layer using texture coordinate x and a level-of-detail derived from the dx and dy
gradients.
B.8.1.14.tex2DLayered()
template<class T>
T tex2DLayered(cudaTextureObject_t texObj,
float x, float y, int layer);
fetches from the CUDA array specified by the two-dimensional texture object texObj
using texture coordinate (x,y) and index layer, as described in Layered Textures.
B.8.1.15.tex2DLayeredLod()
template<class T>
T tex2DLayeredLod(cudaTextureObject_t texObj, float x, float y, int layer,
float level);
fetches from the CUDA array specified by the two-dimensional layered texture at layer
layer using texture coordinate (x,y).
B.8.1.16.tex2DLayeredGrad()
template<class T>
T tex2DLayeredGrad(cudaTextureObject_t texObj, float x, float y, int layer,
float2 dx, float2 dy);
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fetches from the CUDA array specified by the two-dimensional layered texture at layer
layer using texture coordinate (x,y) and a level-of-detail derived from the dx and dy
X and Y gradients.
B.8.1.17.texCubemap()
template<class T>
T texCubemap(cudaTextureObject_t texObj, float x, float y, float z);
fetches the CUDA array specified by the three-dimensional texture object texObj using
texture coordinate (x,y,z), as described in Cubemap Textures.
B.8.1.18.texCubemapLod()
template<class T>
T texCubemapLod(cudaTextureObject_t texObj, float x, float, y, float z,
float level);
fetches from the CUDA array specified by the three-dimensional texture object texObj
using texture coordinate (x,y,z) as described in Cubemap Textures. The level-of-detail
used is given by level.
B.8.1.19.texCubemapLayered()
template<class T>
T texCubemapLayered(cudaTextureObject_t texObj,
float x, float y, float z, int layer);
fetches from the CUDA array specified by the cubemap layered texture object texObj
using texture coordinates (x,y,z), and index layer, as described in Cubemap Layered
Textures.
B.8.1.20.texCubemapLayeredLod()
template<class T>
T texCubemapLayeredLod(cudaTextureObject_t texObj, float x, float y, float z,
int layer, float level);
fetches from the CUDA array specified by the cubemap layered texture object texObj
using texture coordinate (x,y,z) and index layer, as described in Cubemap Layered
Textures, at level-of-detail level level.
B.8.1.21.tex2Dgather()
template<class T>
T tex2Dgather(cudaTextureObject_t texObj,
float x, float y, int comp = 0);
fetches from the CUDA array specified by the 2D texture object texObj using texture
coordinates x and y and the comp parameter as described in Texture Gather.
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B.8.2.Texture Reference API
B.8.2.1.tex1Dfetch()
template<class DataType>
Type tex1Dfetch(
texture<DataType, cudaTextureType1D,
cudaReadModeElementType> texRef,
int x);
float tex1Dfetch(
texture<unsigned char, cudaTextureType1D,
cudaReadModeNormalizedFloat> texRef,
int x);
float tex1Dfetch(
texture<signed char, cudaTextureType1D,
cudaReadModeNormalizedFloat> texRef,
int x);
float tex1Dfetch(
texture<unsigned short, cudaTextureType1D,
cudaReadModeNormalizedFloat> texRef,
int x);
float tex1Dfetch(
texture<signed short, cudaTextureType1D,
cudaReadModeNormalizedFloat> texRef,
int x);
fetches from the region of linear memory bound to the one-dimensional texture
reference texRef using integer texture coordinate x. tex1Dfetch() only works with
non-normalized coordinates, so only the border and clamp addressing modes are
supported. It does not perform any texture filtering. For integer types, it may optionally
promote the integer to single-precision floating point.
Besides the functions shown above, 2-, and 4-tuples are supported; for example:
float4 tex1Dfetch(
texture<uchar4, cudaTextureType1D,
cudaReadModeNormalizedFloat> texRef,
int x);
fetches from the region of linear memory bound to texture reference texRef using
texture coordinate x.
B.8.2.2.tex1D()
template<class DataType, enum cudaTextureReadMode readMode>
Type tex1D(texture<DataType, cudaTextureType1D, readMode> texRef,
float x);
fetches from the CUDA array bound to the one-dimensional texture reference texRef
using texture coordinate x. Type is equal to DataType except when readMode is equal
to cudaReadModeNormalizedFloat (see Texture Reference API), in which case Type is
equal to the matching floating-point type.
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B.8.2.3.tex1DLod()
template<class DataType, enum
cudaTextureReadMode readMode>
Type tex1DLod(texture<DataType, cudaTextureType1D, readMode> texRef, float x,
float level);
fetches from the CUDA array bound to the one-dimensional texture reference texRef
using texture coordinate x. The level-of-detail is given by level. Type is the same as
DataType except when readMode is cudaReadModeNormalizedFloat (see Texture
Reference API), in which case Type is the corresponding floating-point type.
B.8.2.4.tex1DGrad()
template<class DataType, enum
cudaTextureReadMode readMode>
Type tex1DGrad(texture<DataType, cudaTextureType1D, readMode> texRef, float x,
float dx, float dy);
fetches from the CUDA array bound to the one-dimensional texture reference
texRef using texture coordinate x. The level-of-detail is derived from the dx and
dy X- and Y-gradients. Type is the same as DataType except when readMode is
cudaReadModeNormalizedFloat (see Texture Reference API), in which case Type is
the corresponding floating-point type.
B.8.2.5.tex2D()
template<class DataType, enum cudaTextureReadMode readMode>
Type tex2D(texture<DataType, cudaTextureType2D, readMode> texRef,
float x, float y);
fetches from the CUDA array or the region of linear memory bound to the two-
dimensional texture reference texRef using texture coordinates x and y. Type is equal
to DataType except when readMode is equal to cudaReadModeNormalizedFloat (see
Texture Reference API), in which case Type is equal to the matching floating-point type.
B.8.2.6.tex2DLod()
template<class DataType, enum
cudaTextureReadMode readMode>
Type tex2DLod(texture<DataType, cudaTextureType2D, readMode> texRef,
float x, float y, float level);
fetches from the CUDA array bound to the two-dimensional texture reference texRef
using texture coordinate (x,y). The level-of-detail is given by level. Type is the same
as DataType except when readMode is cudaReadModeNormalizedFloat (see Texture
Reference API), in which case Type is the corresponding floating-point type.
B.8.2.7.tex2DGrad()
template<class DataType, enum
cudaTextureReadMode readMode>
Type tex2DGrad(texture<DataType, cudaTextureType2D, readMode> texRef,
float x, float y, float2 dx, float2 dy);
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fetches from the CUDA array bound to the two-dimensional texture reference
texRef using texture coordinate (x,y). The level-of-detail is derived from the dx
and dy X- and Y-gradients. Type is the same as DataType except when readMode is
cudaReadModeNormalizedFloat (see Texture Reference API), in which case Type is
the corresponding floating-point type.
B.8.2.8.tex3D()
template<class DataType, enum cudaTextureReadMode readMode>
Type tex3D(texture<DataType, cudaTextureType3D, readMode> texRef,
float x, float y, float z);
fetches from the CUDA array bound to the three-dimensional texture reference texRef
using texture coordinates x, y, and z. Type is equal to DataType except when readMode
is equal to cudaReadModeNormalizedFloat (see Texture Reference API), in which case
Type is equal to the matching floating-point type.
B.8.2.9.tex3DLod()
template<class DataType, enum
cudaTextureReadMode readMode>
Type tex3DLod(texture<DataType, cudaTextureType3D, readMode> texRef,
float x, float y, float z, float level);
fetches from the CUDA array bound to the two-dimensional texture reference texRef
using texture coordinate (x,y,z). The level-of-detail is given by level. Type is the
same as DataType except when readMode is cudaReadModeNormalizedFloat (see
Texture Reference API), in which case Type is the corresponding floating-point type.
B.8.2.10.tex3DGrad()
template<class DataType, enum
cudaTextureReadMode readMode>
Type tex3DGrad(texture<DataType, cudaTextureType3D, readMode> texRef,
float x, float y, float z, float4 dx, float4 dy);
fetches from the CUDA array bound to the two-dimensional texture reference texRef
using texture coordinate (x,y,z). The level-of-detail is derived from the dx and
dy X- and Y-gradients. Type is the same as DataType except when readMode is
cudaReadModeNormalizedFloat (see Texture Reference API), in which case Type is
the corresponding floating-point type.
B.8.2.11.tex1DLayered()
template<class DataType, enum cudaTextureReadMode readMode>
Type tex1DLayered(
texture<DataType, cudaTextureType1DLayered, readMode> texRef,
float x, int layer);
fetches from the CUDA array bound to the one-dimensional layered texture
reference texRef using texture coordinate x and index layer, as described in
Layered Textures. Type is equal to DataType except when readMode is equal to
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cudaReadModeNormalizedFloat (see Texture Reference API), in which case Type is
equal to the matching floating-point type.
B.8.2.12.tex1DLayeredLod()
template<class DataType, enum cudaTextureReadMode readMode>
Type tex1DLayeredLod(texture<DataType, cudaTextureType1D, readMode> texRef,
float x, int layer, float level);
fetches from the CUDA array bound to the one-dimensional texture reference texRef
using texture coordinate x and index layer as described in Layered Textures. The level-
of-detail is given by level. Type is the same as DataType except when readMode is
cudaReadModeNormalizedFloat (see Texture Reference API), in which case Type is
the corresponding floating-point type.
B.8.2.13.tex1DLayeredGrad()
template<class DataType, enum cudaTextureReadMode readMode>
Type tex1DLayeredGrad(texture<DataType, cudaTextureType1D, readMode> texRef,
float x, int layer, float dx, float dy);
fetches from the CUDA array bound to the one-dimensional texture reference texRef
using texture coordinate x and index layer as described in Layered Textures. The
level-of-detail is derived from the dx and dy X- and Y-gradients. Type is the same as
DataType except when readMode is cudaReadModeNormalizedFloat (see Texture
Reference API), in which case Type is the corresponding floating-point type.
B.8.2.14.tex2DLayered()
template<class DataType, enum cudaTextureReadMode readMode>
Type tex2DLayered(
texture<DataType, cudaTextureType2DLayered, readMode> texRef,
float x, float y, int layer);
fetches from the CUDA array bound to the two-dimensional layered texture
reference texRef using texture coordinates x and y, and index layer, as described
in Texture Memory. Type is equal to DataType except when readMode is equal to
cudaReadModeNormalizedFloat (see Texture Reference API), in which case Type is
equal to the matching floating-point type.
B.8.2.15.tex2DLayeredLod()
template<class DataType, enum cudaTextureReadMode readMode>
Type tex2DLayeredLod(texture<DataType, cudaTextureType2D, readMode> texRef,
float x, float y, int layer, float level);
fetches from the CUDA array bound to the two-dimensional texture reference texRef
using texture coordinate (x,y) and index layer as described in Layered Textures. The
level-of-detail is given by level. Type is the same as DataType except when readMode
is cudaReadModeNormalizedFloat (see Texture Reference API), in which case Type is
the corresponding floating-point type.
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B.8.2.16.tex2DLayeredGrad()
template<class DataType, enum cudaTextureReadMode readMode>
Type tex2DLayeredGrad(texture<DataType, cudaTextureType2D, readMode> texRef,
float x, float y, int layer, float2 dx, float2 dy);
fetches from the CUDA array bound to the two-dimensional texture reference texRef
using texture coordinate (x,y) and index layer as described in Layered Textures. The
level-of-detail is derived from the dx and dy X- and Y-gradients. Type is the same as
DataType except when readMode is cudaReadModeNormalizedFloat (see Texture
Reference API), in which case Type is the corresponding floating-point type.
B.8.2.17.texCubemap()
template<class DataType, enum cudaTextureReadMode readMode>
Type texCubemap(
texture<DataType, cudaTextureTypeCubemap, readMode> texRef,
float x, float y, float z);
fetches from the CUDA array bound to the cubemap texture reference texRef using
texture coordinates x, y, and z, as described in Cubemap Textures. Type is equal to
DataType except when readMode is equal to cudaReadModeNormalizedFloat (see
Texture Reference API), in which case Type is equal to the matching floating-point type.
B.8.2.18.texCubemapLod()
template<class DataType, enum cudaTextureReadMode readMode>
Type texCubemapLod(texture<DataType, cudaTextureType3D, readMode> texRef,
float x, float y, float z, float level);
fetches from the CUDA array bound to the two-dimensional texture reference texRef
using texture coordinate (x,y,z). The level-of-detail is given by level. Type is the
same as DataType except when readMode is cudaReadModeNormalizedFloat (see
Texture Reference API), in which case Type is the corresponding floating-point type.
B.8.2.19.texCubemapLayered()
template<class DataType, enum cudaTextureReadMode readMode>
Type texCubemapLayered(
texture<DataType, cudaTextureTypeCubemapLayered, readMode> texRef,
float x, float y, float z, int layer);
fetches from the CUDA array bound to the cubemap layered texture reference texRef
using texture coordinates x, y, and z, and index layer, as described in Cubemap
Layered Textures. Type is equal to DataType except when readMode is equal to
cudaReadModeNormalizedFloat (see Texture Reference API), in which case Type is
equal to the matching floating-point type.
B.8.2.20.texCubemapLayeredLod()
template<class DataType, enum cudaTextureReadMode readMode>
Type texCubemapLayeredLod(texture<DataType, cudaTextureType3D, readMode> texRef,
float x, float y, float z, int layer, float level);
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fetches from the CUDA array bound to the two-dimensional texture reference texRef
using texture coordinate (x,y,z) and index layer as described in Layered Textures.
The level-of-detail is given by level. Type is the same as DataType except when
readMode is cudaReadModeNormalizedFloat (see Texture Reference API), in which
case Type is the corresponding floating-point type.
B.8.2.21.tex2Dgather()
template<class DataType, enum cudaTextureReadMode readMode>
Type tex2Dgather(
texture<DataType, cudaTextureType2D, readMode> texRef,
float x, float y, int comp = 0);
fetches from the CUDA array bound to the 2D texture reference texRef using texture
coordinates x and y and the comp parameter as described in Texture Gather. Type is a 4-
component vector type. It is based on the base type of DataType except when readMode
is equal to cudaReadModeNormalizedFloat (see Texture Reference API), in which case
it is always float4.
B.9.Surface Functions
Surface functions are only supported by devices of compute capability 2.0 and higher.
Surface objects are described in described in Surface Object API
Surface references are described in Surface Reference API.
In the sections below, boundaryMode specifies the boundary mode, that is how out-of-
range surface coordinates are handled; it is equal to either cudaBoundaryModeClamp,
in which case out-of-range coordinates are clamped to the valid range, or
cudaBoundaryModeZero, in which case out-of-range reads return zero and out-of-range
writes are ignored, or cudaBoundaryModeTrap, in which case out-of-range accesses
cause the kernel execution to fail.
B.9.1.Surface Object API
B.9.1.1.surf1Dread()
template<class T>
T surf1Dread(cudaSurfaceObject_t surfObj, int x,
boundaryMode = cudaBoundaryModeTrap);
reads the CUDA array specified by the one-dimensional surface object surfObj using
coordinate x.
B.9.1.2.surf1Dwrite
template<class T>
void surf1Dwrite(T data,
cudaSurfaceObject_t surfObj,
int x,
boundaryMode = cudaBoundaryModeTrap);
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writes value data to the CUDA array specified by the one-dimensional surface object
surfObj at coordinate x.
B.9.1.3.surf2Dread()
template<class T>
T surf2Dread(cudaSurfaceObject_t surfObj,
int x, int y,
boundaryMode = cudaBoundaryModeTrap);
template<class T>
void surf2Dread(T* data,
cudaSurfaceObject_t surfObj,
int x, int y,
boundaryMode = cudaBoundaryModeTrap);
reads the CUDA array specified by the two-dimensional surface object surfObj using
coordinates x and y.
B.9.1.4.surf2Dwrite()
template<class T>
void surf2Dwrite(T data,
cudaSurfaceObject_t surfObj,
int x, int y,
boundaryMode = cudaBoundaryModeTrap);
writes value data to the CUDA array specified by the two-dimensional surface object
surfObj at coordinate x and y.
B.9.1.5.surf3Dread()
template<class T>
T surf3Dread(cudaSurfaceObject_t surfObj,
int x, int y, int z,
boundaryMode = cudaBoundaryModeTrap);
template<class T>
void surf3Dread(T* data,
cudaSurfaceObject_t surfObj,
int x, int y, int z,
boundaryMode = cudaBoundaryModeTrap);
reads the CUDA array specified by the three-dimensional surface object surfObj using
coordinates x, y, and z.
B.9.1.6.surf3Dwrite()
template<class T>
void surf3Dwrite(T data,
cudaSurfaceObject_t surfObj,
int x, int y, int z,
boundaryMode = cudaBoundaryModeTrap);
writes value data to the CUDA array specified by the three-dimensional object surfObj
at coordinate x, y, and z.
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B.9.1.7.surf1DLayeredread()
template<class T>
T surf1DLayeredread(
cudaSurfaceObject_t surfObj,
int x, int layer,
boundaryMode = cudaBoundaryModeTrap);
template<class T>
void surf1DLayeredread(T data,
cudaSurfaceObject_t surfObj,
int x, int layer,
boundaryMode = cudaBoundaryModeTrap);
reads the CUDA array specified by the one-dimensional layered surface object surfObj
using coordinate x and index layer.
B.9.1.8.surf1DLayeredwrite()
template<class Type>
void surf1DLayeredwrite(T data,
cudaSurfaceObject_t surfObj,
int x, int layer,
boundaryMode = cudaBoundaryModeTrap);
writes value data to the CUDA array specified by the two-dimensional layered surface
object surfObj at coordinate x and index layer.
B.9.1.9.surf2DLayeredread()
template<class T>
T surf2DLayeredread(
cudaSurfaceObject_t surfObj,
int x, int y, int layer,
boundaryMode = cudaBoundaryModeTrap);
template<class T>
void surf2DLayeredread(T data,
cudaSurfaceObject_t surfObj,
int x, int y, int layer,
boundaryMode = cudaBoundaryModeTrap);
reads the CUDA array specified by the two-dimensional layered surface object surfObj
using coordinate x and y, and index layer.
B.9.1.10.surf2DLayeredwrite()
template<class T>
void surf2DLayeredwrite(T data,
cudaSurfaceObject_t surfObj,
int x, int y, int layer,
boundaryMode = cudaBoundaryModeTrap);
writes value data to the CUDA array specified by the one-dimensional layered surface
object surfObj at coordinate x and y, and index layer.
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B.9.1.11.surfCubemapread()
template<class T>
T surfCubemapread(
cudaSurfaceObject_t surfObj,
int x, int y, int face,
boundaryMode = cudaBoundaryModeTrap);
template<class T>
void surfCubemapread(T data,
cudaSurfaceObject_t surfObj,
int x, int y, int face,
boundaryMode = cudaBoundaryModeTrap);
reads the CUDA array specified by the cubemap surface object surfObj using
coordinate x and y, and face index face.
B.9.1.12.surfCubemapwrite()
template<class T>
void surfCubemapwrite(T data,
cudaSurfaceObject_t surfObj,
int x, int y, int face,
boundaryMode = cudaBoundaryModeTrap);
writes value data to the CUDA array specified by the cubemap object surfObj at
coordinate x and y, and face index face.
B.9.1.13.surfCubemapLayeredread()
template<class T>
T surfCubemapLayeredread(
cudaSurfaceObject_t surfObj,
int x, int y, int layerFace,
boundaryMode = cudaBoundaryModeTrap);
template<class T>
void surfCubemapLayeredread(T data,
cudaSurfaceObject_t surfObj,
int x, int y, int layerFace,
boundaryMode = cudaBoundaryModeTrap);
reads the CUDA array specified by the cubemap layered surface object surfObj using
coordinate x and y, and index layerFace.
B.9.1.14.surfCubemapLayeredwrite()
template<class T>
void surfCubemapLayeredwrite(T data,
cudaSurfaceObject_t surfObj,
int x, int y, int layerFace,
boundaryMode = cudaBoundaryModeTrap);
writes value data to the CUDA array specified by the cubemap layered object surfObj at
coordinate x and y, and index layerFace.
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B.9.2.Surface Reference API
B.9.2.1.surf1Dread()
template<class Type>
Type surf1Dread(surface<void, cudaSurfaceType1D> surfRef,
int x,
boundaryMode = cudaBoundaryModeTrap);
template<class Type>
void surf1Dread(Type data,
surface<void, cudaSurfaceType1D> surfRef,
int x,
boundaryMode = cudaBoundaryModeTrap);
reads the CUDA array bound to the one-dimensional surface reference surfRef using
coordinate x.
B.9.2.2.surf1Dwrite
template<class Type>
void surf1Dwrite(Type data,
surface<void, cudaSurfaceType1D> surfRef,
int x,
boundaryMode = cudaBoundaryModeTrap);
writes value data to the CUDA array bound to the one-dimensional surface reference
surfRef at coordinate x.
B.9.2.3.surf2Dread()
template<class Type>
Type surf2Dread(surface<void, cudaSurfaceType2D> surfRef,
int x, int y,
boundaryMode = cudaBoundaryModeTrap);
template<class Type>
void surf2Dread(Type* data,
surface<void, cudaSurfaceType2D> surfRef,
int x, int y,
boundaryMode = cudaBoundaryModeTrap);
reads the CUDA array bound to the two-dimensional surface reference surfRef using
coordinates x and y.
B.9.2.4.surf2Dwrite()
template<class Type>
void surf3Dwrite(Type data,
surface<void, cudaSurfaceType3D> surfRef,
int x, int y, int z,
boundaryMode = cudaBoundaryModeTrap);
writes value data to the CUDA array bound to the two-dimensional surface reference
surfRef at coordinate x and y.
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B.9.2.5.surf3Dread()
template<class Type>
Type surf3Dread(surface<void, cudaSurfaceType3D> surfRef,
int x, int y, int z,
boundaryMode = cudaBoundaryModeTrap);
template<class Type>
void surf3Dread(Type* data,
surface<void, cudaSurfaceType3D> surfRef,
int x, int y, int z,
boundaryMode = cudaBoundaryModeTrap);
reads the CUDA array bound to the three-dimensional surface reference surfRef using
coordinates x, y, and z.
B.9.2.6.surf3Dwrite()
template<class Type>
void surf3Dwrite(Type data,
surface<void, cudaSurfaceType3D> surfRef,
int x, int y, int z,
boundaryMode = cudaBoundaryModeTrap);
writes value data to the CUDA array bound to the three-dimensional surface reference
surfRef at coordinate x, y, and z.
B.9.2.7.surf1DLayeredread()
template<class Type>
Type surf1DLayeredread(
surface<void, cudaSurfaceType1DLayered> surfRef,
int x, int layer,
boundaryMode = cudaBoundaryModeTrap);
template<class Type>
void surf1DLayeredread(Type data,
surface<void, cudaSurfaceType1DLayered> surfRef,
int x, int layer,
boundaryMode = cudaBoundaryModeTrap);
reads the CUDA array bound to the one-dimensional layered surface reference surfRef
using coordinate x and index layer.
B.9.2.8.surf1DLayeredwrite()
template<class Type>
void surf1DLayeredwrite(Type data,
surface<void, cudaSurfaceType1DLayered> surfRef,
int x, int layer,
boundaryMode = cudaBoundaryModeTrap);
writes value data to the CUDA array bound to the two-dimensional layered surface
reference surfRef at coordinate x and index layer.
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B.9.2.9.surf2DLayeredread()
template<class Type>
Type surf2DLayeredread(
surface<void, cudaSurfaceType2DLayered> surfRef,
int x, int y, int layer,
boundaryMode = cudaBoundaryModeTrap);
template<class Type>
void surf2DLayeredread(Type data,
surface<void, cudaSurfaceType2DLayered> surfRef,
int x, int y, int layer,
boundaryMode = cudaBoundaryModeTrap);
reads the CUDA array bound to the two-dimensional layered surface reference surfRef
using coordinate x and y, and index layer.
B.9.2.10.surf2DLayeredwrite()
template<class Type>
void surf2DLayeredwrite(Type data,
surface<void, cudaSurfaceType2DLayered> surfRef,
int x, int y, int layer,
boundaryMode = cudaBoundaryModeTrap);
writes value data to the CUDA array bound to the one-dimensional layered surface
reference surfRef at coordinate x and y, and index layer.
B.9.2.11.surfCubemapread()
template<class Type>
Type surfCubemapread(
surface<void, cudaSurfaceTypeCubemap> surfRef,
int x, int y, int face,
boundaryMode = cudaBoundaryModeTrap);
template<class Type>
void surfCubemapread(Type data,
surface<void, cudaSurfaceTypeCubemap> surfRef,
int x, int y, int face,
boundaryMode = cudaBoundaryModeTrap);
reads the CUDA array bound to the cubemap surface reference surfRef using
coordinate x and y, and face index face.
B.9.2.12.surfCubemapwrite()
template<class Type>
void surfCubemapwrite(Type data,
surface<void, cudaSurfaceTypeCubemap> surfRef,
int x, int y, int face,
boundaryMode = cudaBoundaryModeTrap);
writes value data to the CUDA array bound to the cubemap reference surfRef at
coordinate x and y, and face index face.
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B.9.2.13.surfCubemapLayeredread()
template<class Type>
Type surfCubemapLayeredread(
surface<void, cudaSurfaceTypeCubemapLayered> surfRef,
int x, int y, int layerFace,
boundaryMode = cudaBoundaryModeTrap);
template<class Type>
void surfCubemapLayeredread(Type data,
surface<void, cudaSurfaceTypeCubemapLayered> surfRef,
int x, int y, int layerFace,
boundaryMode = cudaBoundaryModeTrap);
reads the CUDA array bound to the cubemap layered surface reference surfRef using
coordinate x and y, and index layerFace.
B.9.2.14.surfCubemapLayeredwrite()
template<class Type>
void surfCubemapLayeredwrite(Type data,
surface<void, cudaSurfaceTypeCubemapLayered> surfRef,
int x, int y, int layerFace,
boundaryMode = cudaBoundaryModeTrap);
writes value data to the CUDA array bound to the cubemap layered reference surfRef
at coordinate x and y, and index layerFace.
B.10.Read-Only Data Cache Load Function
The read-only data cache load function is only supported by devices of compute
capability 3.5 and higher.
T __ldg(const T* address);
returns the data of type T located at address address, where T is char, short, int,
long long unsigned char, unsigned short, unsigned int, unsigned long
long, int2, int4, uint2, uint4, float, float2, float4, double, or double2. The
operation is cached in the read-only data cache (see Global Memory).
B.11.Time Function
clock_t clock();
long long int clock64();
when executed in device code, returns the value of a per-multiprocessor counter that is
incremented every clock cycle. Sampling this counter at the beginning and at the end of
a kernel, taking the difference of the two samples, and recording the result per thread
provides a measure for each thread of the number of clock cycles taken by the device to
completely execute the thread, but not of the number of clock cycles the device actually
spent executing thread instructions. The former number is greater than the latter since
threads are time sliced.
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B.12.Atomic Functions
An atomic function performs a read-modify-write atomic operation on one 32-bit or 64-
bit word residing in global or shared memory. For example, atomicAdd() reads a word
at some address in global or shared memory, adds a number to it, and writes the result
back to the same address. The operation is atomic in the sense that it is guaranteed to
be performed without interference from other threads. In other words, no other thread
can access this address until the operation is complete. Atomic functions do not act as
memory fences and do not imply synchronization or ordering constraints for memory
operations (see Memory Fence Functions for more details on memory fences). Atomic
functions can only be used in device functions.
On GPU architectures with compute capability lower than 6.x, atomics operations done
from the GPU are atomic only with respect to that GPU. If the GPU attempts an atomic
operation to a peer GPU’s memory, the operation appears as a regular read followed
by a write to the peer GPU, and the two operations are not done as one single atomic
operation. Similarly, atomic operations from the GPU to CPU memory will not be atomic
with respect to CPU initiated atomic operations.
Compute capability 6.x introduces new type of atomics which allows developers to
widen or narrow the scope of an atomic operation. For example, atomicAdd_system
guarantees that the instruction is atomic with respect to other CPUs and GPUs in the
system. atomicAdd_block implies that the instruction is atomic only with respect
atomics from other threads in the same thread block. In the following example both CPU
and GPU can atomically update integer value at address addr:
__global__ void mykernel(int *addr) {
atomicAdd_system(addr, 10); // only available on devices with compute
capability 6.x
}
void foo() {
int *addr;
cudaMallocManaged(&addr, 4);
*addr = 0;
mykernel<<<...>>>(addr);
__sync_fetch_and_add(addr, 10); // CPU atomic operation
}
The new scoped versions of atomics are available for all atomics listed below only for
compute capabilities 6.x and later.
Note that any atomic operation can be implemented based on atomicCAS() (Compare
And Swap). For example, atomicAdd() for double-precision floating-point numbers
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is not available on devices with compute capability lower than 6.0 but it can be
implemented as follows:
#if __CUDA_ARCH__ < 600
__device__ double atomicAdd(double* address, double val)
{
unsigned long long int* address_as_ull =
(unsigned long long int*)address;
unsigned long long int old = *address_as_ull, assumed;
do {
assumed = old;
old = atomicCAS(address_as_ull, assumed,
__double_as_longlong(val +
__longlong_as_double(assumed)));
// Note: uses integer comparison to avoid hang in case of NaN (since NaN !=
NaN)
} while (assumed != old);
return __longlong_as_double(old);
}
#endif
B.12.1.Arithmetic Functions
B.12.1.1.atomicAdd()
int atomicAdd(int* address, int val);
unsigned int atomicAdd(unsigned int* address,
unsigned int val);
unsigned long long int atomicAdd(unsigned long long int* address,
unsigned long long int val);
float atomicAdd(float* address, float val);
double atomicAdd(double* address, double val);
reads the 32-bit or 64-bit word old located at the address address in global or shared
memory, computes (old + val), and stores the result back to memory at the same
address. These three operations are performed in one atomic transaction. The function
returns old.
The 32-bit floating-point version of atomicAdd() is only supported by devices of
compute capability 2.x and higher.
The 64-bit floating-point version of atomicAdd() is only supported by devices of
compute capability 6.x and higher.
B.12.1.2.atomicSub()
int atomicSub(int* address, int val);
unsigned int atomicSub(unsigned int* address,
unsigned int val);
reads the 32-bit word old located at the address address in global or shared memory,
computes (old - val), and stores the result back to memory at the same address.
These three operations are performed in one atomic transaction. The function returns
old.
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B.12.1.3.atomicExch()
int atomicExch(int* address, int val);
unsigned int atomicExch(unsigned int* address,
unsigned int val);
unsigned long long int atomicExch(unsigned long long int* address,
unsigned long long int val);
float atomicExch(float* address, float val);
reads the 32-bit or 64-bit word old located at the address address in global or shared
memory and stores val back to memory at the same address. These two operations are
performed in one atomic transaction. The function returns old.
B.12.1.4.atomicMin()
int atomicMin(int* address, int val);
unsigned int atomicMin(unsigned int* address,
unsigned int val);
unsigned long long int atomicMin(unsigned long long int* address,
unsigned long long int val);
reads the 32-bit or 64-bit word old located at the address address in global or shared
memory, computes the minimum of old and val, and stores the result back to memory
at the same address. These three operations are performed in one atomic transaction.
The function returns old.
The 64-bit version of atomicMin() is only supported by devices of compute capability
3.5 and higher.
B.12.1.5.atomicMax()
int atomicMax(int* address, int val);
unsigned int atomicMax(unsigned int* address,
unsigned int val);
unsigned long long int atomicMax(unsigned long long int* address,
unsigned long long int val);
reads the 32-bit or 64-bit word old located at the address address in global or shared
memory, computes the maximum of old and val, and stores the result back to memory
at the same address. These three operations are performed in one atomic transaction.
The function returns old.
The 64-bit version of atomicMax() is only supported by devices of compute capability
3.5 and higher.
B.12.1.6.atomicInc()
unsigned int atomicInc(unsigned int* address,
unsigned int val);
reads the 32-bit word old located at the address address in global or shared memory,
computes ((old >= val) ? 0 : (old+1)), and stores the result back to memory at
the same address. These three operations are performed in one atomic transaction. The
function returns old.
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B.12.1.7.atomicDec()
unsigned int atomicDec(unsigned int* address,
unsigned int val);
reads the 32-bit word old located at the address address in global or shared memory,
computes (((old == 0) | (old > val)) ? val : (old-1) ), and stores the
result back to memory at the same address. These three operations are performed in one
atomic transaction. The function returns old.
B.12.1.8.atomicCAS()
int atomicCAS(int* address, int compare, int val);
unsigned int atomicCAS(unsigned int* address,
unsigned int compare,
unsigned int val);
unsigned long long int atomicCAS(unsigned long long int* address,
unsigned long long int compare,
unsigned long long int val);
reads the 32-bit or 64-bit word old located at the address address in global or shared
memory, computes (old == compare ? val : old) , and stores the result back
to memory at the same address. These three operations are performed in one atomic
transaction. The function returns old (Compare And Swap).
B.12.2.Bitwise Functions
B.12.2.1.atomicAnd()
int atomicAnd(int* address, int val);
unsigned int atomicAnd(unsigned int* address,
unsigned int val);
unsigned long long int atomicAnd(unsigned long long int* address,
unsigned long long int val);
reads the 32-bit or 64-bit word old located at the address address in global or shared
memory, computes (old & val), and stores the result back to memory at the same
address. These three operations are performed in one atomic transaction. The function
returns old.
The 64-bit version of atomicAnd() is only supported by devices of compute capability
3.5 and higher.
B.12.2.2.atomicOr()
int atomicOr(int* address, int val);
unsigned int atomicOr(unsigned int* address,
unsigned int val);
unsigned long long int atomicOr(unsigned long long int* address,
unsigned long long int val);
reads the 32-bit or 64-bit word old located at the address address in global or shared
memory, computes (old | val), and stores the result back to memory at the same
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address. These three operations are performed in one atomic transaction. The function
returns old.
The 64-bit version of atomicOr() is only supported by devices of compute capability
3.5 and higher.
B.12.2.3.atomicXor()
int atomicXor(int* address, int val);
unsigned int atomicXor(unsigned int* address,
unsigned int val);
unsigned long long int atomicXor(unsigned long long int* address,
unsigned long long int val);
reads the 32-bit or 64-bit word old located at the address address in global or shared
memory, computes (old ^ val), and stores the result back to memory at the same
address. These three operations are performed in one atomic transaction. The function
returns old.
The 64-bit version of atomicXor() is only supported by devices of compute capability
3.5 and higher.
B.13.Warp Vote Functions
int __all_sync(unsigned mask, int predicate);
int __any_sync(unsigned mask, int predicate);
unsigned __ballot_sync(unsigned mask, int predicate);
unsigned __activemask();
Deprecation notice: __any, __all, and __ballot have been deprecated as of CUDA 9.0.
The warp vote functions allow the threads of a given warp to perform a reduction-and-
broadcast operation. These functions take as input an integer predicate from each
thread in the warp and compare those values with zero. The results of the comparisons
are combined (reduced) across the active threads of the warp in one of the following
ways, broadcasting a single return value to each participating thread:
__all_sync(unsigned mask, predicate):
Evaluate predicate for all non-exited threads in mask and return non-zero if and
only if predicate evaluates to non-zero for all of them.
__any_sync(unsigned mask, predicate):
Evaluate predicate for all non-exited threads in mask and return non-zero if and
only if predicate evaluates to non-zero for any of them.
__ballot_sync(unsigned mask, predicate):
Evaluate predicate for all non-exited threads in mask and return an integer whose
Nth bit is set if and only if predicate evaluates to non-zero for the Nth thread of the
warp and the Nth thread is active.
__activemask():
Returns a 32-bit integer mask of all currently active threads in the calling warp.
The Nth bit is set if the Nth lane in the warp is active when __activemask() is
called. Inactive threads are represented by 0 bits in the returned mask. Threads
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which have exited the program are always marked as inactive. Note that threads that
are convergent at an __activemask() call are not guaranteed to be convergent at
subsequent instructions unless those instructions are synchronizing warp-builtin
functions.
Notes
For __all_sync, __any_sync, and __ballot_sync, a mask must be passed that
specifies the threads participating in the call. A bit, representing the thread's lane ID,
must be set for each participating thread to ensure they are properly converged before
the intrinsic is executed by the hardware. All active threads named in mask must
execute the same intrinsic with the same mask, or the result is undefined.
B.14.Warp Match Functions
__match_any_sync and __match_all_sync perform a broadcast-and-compare
operation of a variable between threads within a warp.
Supported by devices of compute capability 7.x or higher.
B.14.1.Synopsys
unsigned int __match_any_sync(unsigned mask, T value);
unsigned int __match_all_sync(unsigned mask, T value, int
*pred);
T can be int, unsigned int, long, unsigned long, long long, unsigned long
long, float or double.
B.14.2.Description
The __match_sync() intrinsics permit a broadcast-and-compare of a value value
across threads in a warp after synchronizing threads named in mask.
__match_any_sync
Returns mask of threads that have same value of value in mask
__match_all_sync
Returns mask if all threads in mask have the same value for value; otherwise 0 is
returned. Predicate pred is set to true if all threads in mask have the same value of
value; otherwise the predicate is set to false.
The new *_sync match intrinsics take in a mask indicating the threads participating in
the call. A bit, representing the thread's lane id, must be set for each participating thread
to ensure they are properly converged before the intrinsic is executed by the hardware.
All non-exited threads named in mask must execute the same intrinsic with the same
mask, or the result is undefined.
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B.15.Warp Shuffle Functions
__shfl_sync, __shfl_up_sync, __shfl_down_sync, and __shfl_xor_sync
exchange a variable between threads within a warp.
Supported by devices of compute capability 3.x or higher.
Deprecation Notice: __shfl, __shfl_up, __shfl_down, and __shfl_xor have been
deprecated as of CUDA 9.0.
B.15.1.Synopsis
T __shfl_sync(unsigned mask, T var, int srcLane, int width=warpSize);
T __shfl_up_sync(unsigned mask, T var, unsigned int delta, int width=warpSize);
T __shfl_down_sync(unsigned mask, T var, unsigned int delta, int
width=warpSize);
T __shfl_xor_sync(unsigned mask, T var, int laneMask, int width=warpSize);
T can be int, unsigned int, long, unsigned long, long long, unsigned long
long, float or double. With the cuda_fp16.h header included, T can also be __half
or __half2.
B.15.2.Description
The __shfl_sync() intrinsics permit exchanging of a variable between threads within
a warp without use of shared memory. The exchange occurs simultaneously for all active
threads within the warp (and named in mask), moving 4 or 8 bytes of data per thread
depending on the type.
Threads within a warp are referred to as lanes, and may have an index between 0 and
warpSize-1 (inclusive). Four source-lane addressing modes are supported:
__shfl_sync()
Direct copy from indexed lane
__shfl_up_sync()
Copy from a lane with lower ID relative to caller
__shfl_down_sync()
Copy from a lane with higher ID relative to caller
__shfl_xor_sync()
Copy from a lane based on bitwise XOR of own lane ID
Threads may only read data from another thread which is actively participating in
the __shfl_sync() command. If the target thread is inactive, the retrieved value is
undefined.
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All of the __shfl_sync() intrinsics take an optional width parameter which alters
the behavior of the intrinsic. width must have a value which is a power of 2; results are
undefined if width is not a power of 2, or is a number greater than warpSize.
__shfl_sync() returns the value of var held by the thread whose ID is given by
srcLane. If width is less than warpSize then each subsection of the warp behaves as
a separate entity with a starting logical lane ID of 0. If srcLane is outside the range
[0:width-1], the value returned corresponds to the value of var held by the srcLane
modulo width (i.e. within the same subsection).
__shfl_up_sync() calculates a source lane ID by subtracting delta from the caller's
lane ID. The value of var held by the resulting lane ID is returned: in effect, var is
shifted up the warp by delta lanes. If width is less than warpSize then each subsection
of the warp behaves as a separate entity with a starting logical lane ID of 0. The source
lane index will not wrap around the value of width, so effectively the lower delta lanes
will be unchanged.
__shfl_down_sync() calculates a source lane ID by adding delta to the caller's lane
ID. The value of var held by the resulting lane ID is returned: this has the effect of
shifting var down the warp by delta lanes. If width is less than warpSize then each
subsection of the warp behaves as a separate entity with a starting logical lane ID of 0.
As for __shfl_up_sync(), the ID number of the source lane will not wrap around the
value of width and so the upper delta lanes will remain unchanged.
__shfl_xor_sync() calculates a source line ID by performing a bitwise XOR of
the caller's lane ID with laneMask: the value of var held by the resulting lane ID is
returned. If width is less than warpSize then each group of width consecutive threads
are able to access elements from earlier groups of threads, however if they attempt to
access elements from later groups of threads their own value of var will be returned.
This mode implements a butterfly addressing pattern such as is used in tree reduction
and broadcast.
The new *_sync shfl intrinsics take in a mask indicating the threads participating in the
call. A bit, representing the thread's lane id, must be set for each participating thread to
ensure they are properly converged before the intrinsic is executed by the hardware. All
non-exited threads named in mask must execute the same intrinsic with the same mask,
or the result is undefined.
B.15.3.Return Value
All __shfl_sync() intrinsics return the 4-byte word referenced by var from the source
lane ID as an unsigned integer. If the source lane ID is out of range or the source thread
has exited, the calling thread's own var is returned.
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B.15.4.Notes
Threads may only read data from another thread which is actively participating in
the __shfl_sync() command. If the target thread is inactive, the retrieved value is
undefined.
width must be a power-of-2 (i.e., 2, 4, 8, 16 or 32). Results are unspecified for other
values.
B.15.5.Examples
B.15.5.1.Broadcast of a single value across a warp
#include <stdio.h>
__global__ void bcast(int arg) {
int laneId = threadIdx.x & 0x1f;
int value;
if (laneId == 0) // Note unused variable for
value = arg; // all threads except lane 0
value = __shfl_sync(0xffffffff, value, 0); // Synchronize all threads in
warp, and get "value" from lane 0
if (value != arg)
printf("Thread %d failed.\n", threadIdx.x);
}
int main() {
bcast<<< 1, 32 >>>(1234);
cudaDeviceSynchronize();
return 0;
}
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B.15.5.2.Inclusive plus-scan across sub-partitions of 8 threads
#include <stdio.h>
__global__ void scan4() {
int laneId = threadIdx.x & 0x1f;
// Seed sample starting value (inverse of lane ID)
int value = 31 - laneId;
// Loop to accumulate scan within my partition.
// Scan requires log2(n) == 3 steps for 8 threads
// It works by an accumulated sum up the warp
// by 1, 2, 4, 8 etc. steps.
for (int i=1; i<=4; i*=2) {
// We do the __shfl_sync unconditionally so that we
// can read even from threads which won't do a
// sum, and then conditionally assign the result.
int n = __shfl_up_sync(0xffffffff, value, i, 8);
if ((laneId & 7) >= i)
value += n;
}
printf("Thread %d final value = %d\n", threadIdx.x, value);
}
int main() {
scan4<<< 1, 32 >>>();
cudaDeviceSynchronize();
return 0;
}
B.15.5.3.Reduction across a warp
#include <stdio.h>
__global__ void warpReduce() {
int laneId = threadIdx.x & 0x1f;
// Seed starting value as inverse lane ID
int value = 31 - laneId;
// Use XOR mode to perform butterfly reduction
for (int i=16; i>=1; i/=2)
value += __shfl_xor_sync(0xffffffff, value, i, 32);
// "value" now contains the sum across all threads
printf("Thread %d final value = %d\n", threadIdx.x, value);
}
int main() {
warpReduce<<< 1, 32 >>>();
cudaDeviceSynchronize();
return 0;
}
B.16.Warp matrix functions [PREVIEW FEATURE]
C++ warp matrix operations leverage Tensor Cores to accelerate matrix problems of the
form D=A*B+C. This requires co-operation from all threads in a warp.
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These warp matrix functions are a preview feature supported by devices of compute
capability 7.0 or higher. The data structures and APIs described here are subject to
change in future releases, and may not be compatible with those future releases.
B.16.1.Description
All following functions and types are defined in the namespace nvcuda::wmma.
template<typename Use, int m, int n, int k, typename T, typename Layout=void>
class fragment;
void load_matrix_sync(fragment<...> &a, const T* mptr, unsigned ldm);
void load_matrix_sync(fragment<...> &a, const T* mptr, unsigned ldm, layout_t
layout);
void store_matrix_sync(T* mptr, const fragment<...> &a, unsigned ldm, layout_t
layout);
void fill_fragment(fragment<...> &a, const T& v);
void mma_sync(fragment<...> &d, const fragment<...> &a, const fragment<...>
&b, const fragment<...> &c, bool satf=false);
fragment
An overloaded class containing a section of a matrix distributed across all threads
in the warp. The mapping of matrix elements into fragment internal storage is
unspecified and subject to change in future architectures.
Only certain combinations of template arguments are allowed. The first template
parameter specifies how the fragment will participate in the matrix operation.
Acceptable values for Use are:
‣matrix_a when the fragment is used as the first multiplicand, A,
‣matrix_b when the fragment is used as the second multiplicand, B, or
‣accumulator when the fragment is used as the source or destination
accumulators (C or D, respectively).
The m, n and k sizes describe the shape of the warp-wide matrix tiles participating
in the multiply-accumulate operation. The dimension of each tile depends on its
role. For matrix_a the tile takes dimension m x k; for matrix_b the dimension
is k x n, and accumulator tiles are m x n. Only the three following (m, n, k)
configurations are supported: (16, 16, 16), (32, 8, 16), and (8, 32, 16).
The data type, T, must be __half for multiplicands, and can be either __half or
float for accumulators. The Layout parameter must be specified for matrix_a and
matrix_b fragments. row_major or col_major indicate that elements within a
matrix row or column are contiguous in memory, respectively. The Layout parameter
for an accumulator matrix should retain the default value of void. A row or column
layout is specified only when the accumulator is loaded or stored as described below.
load_matrix_sync
Waits until all threads in the warp are converged and then loads the matrix fragment
a from memory. mptr must be a 128-bit aligned pointer pointing to the first element
of the matrix in memory. ldm describes the stride in elements between consecutive
rows (for row major layout) or columns (for column major layout) and must be a
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multiple of 16 bytes (i.e., 8 __half elements or 4 float elements). If the fragment is
an accumulator, the layout argument must be specified as either mem_row_major or
mem_col_major. For matrix_a and matrix_b fragments, the layout is inferred from
the fragment's Layout parameter. The values of mptr, ldm, layout and all template
parameters for a must be the same for all threads in the warp. This function must be
called by all threads in the warp, or the result is undefined.
store_matrix_sync
Waits until all threads in the warp are converged and then stores the matrix fragment
a to memory. mptr must be a 128-bit aligned pointer pointing to the first element
of the matrix in memory. ldm describes the stride in elements between consecutive
rows (for row major layout) or columns (for column major layout) and must be a
multiple of 16 bytes. The layout of the output matrix must be specified as either
mem_row_major or mem_col_major. The values of mptr, ldm, layout and all
template parameters for a must be the same for all threads in the warp. This function
must be called by all threads in the warp, or the result is undefined.
fill_fragment
Fill a matrix fragment with a constant value v. Because the mapping of matrix
elements to each fragment is unspecified, this function is ordinarily called by all
threads in the warp with a common value for v.
mma_sync
Waits until all threads in the warp are converged and then performs the warp-
synchronous matrix multiply-accumulate operation D=A*B+C. The in-place operation,
C=A*B+C, is also supported. The value of satf and template parameters for each
matrix fragment must be the same for all threads in the warp. Also, the template
parameters m, n and k must match between fragements A, B, C and D. This function
must be called by all threads in the warp, or the result is undefined.
If satf (saturate to finite value) mode is true, the following additional numerical
properties apply for the destination accumulator:
‣If an element result is +Infinity, the corresponding accumulator will contain
+MAX_NORM
‣If an element result is -Infinity, the corresponding accumulator will contain -
MAX_NORM
‣If an element result is NaN, the corresponding accumulator will contain +0
Because the map of matrix elements into each thread's fragment is unspecified,
individual matrix elements must be accessed from memory (shared or global) after
calling store_matrix_sync. In the special case where all threads in the warp will
apply an element-wise operation uniformly to all fragment elements, direct element
access can be implemented using the following fragment class members.
enum fragment<Use, m, n, k, T, Layout>::num_elements;
T fragment<Use, m, n, k, T, Layout>::x[num_elements];
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As an example, the following code scales an accumulator matrix tile by half.
wmma::fragment<wmma::accumulator, 16, 16, 16, float> frag;
float alpha = 0.5f; // Same value for all threads in warp
...
for(int t=0; t<frag.num_elements; t++)
frag.x[t] *= alpha;
B.16.2.Example
The following code implements a 16x16x16 matrix multiplication in a single warp.
#include <mma.h>
using namespace nvcuda;
__global__ void wmma_ker(half *a, half *b, float *c) {
// Declare the fragments
wmma::fragment<wmma::matrix_a, 16, 16, 16, half, wmma::col_major> a_frag;
wmma::fragment<wmma::matrix_b, 16, 16, 16, half, wmma::row_major> b_frag;
wmma::fragment<wmma::accumulator, 16, 16, 16, float> c_frag;
// Initialize the output to zero
wmma::fill_fragment(c_frag, 0.0f);
// Load the inputs
wmma::load_matrix_sync(a_frag, a, 16);
wmma::load_matrix_sync(b_frag, b, 16);
// Perform the matrix multiplication
wmma::mma_sync(c_frag, a_frag, b_frag, c_frag);
// Store the output
wmma::store_matrix_sync(c, c_frag, 16, wmma::mem_row_major);
}
B.17.Profiler Counter Function
Each multiprocessor has a set of sixteen hardware counters that an application can
increment with a single instruction by calling the __prof_trigger() function.
void __prof_trigger(int counter);
increments by one per warp the per-multiprocessor hardware counter of index counter.
Counters 8 to 15 are reserved and should not be used by applications.
The value of counters 0, 1, ..., 7 can be obtained via nvprof by nvprof --events
prof_trigger_0x where x is 0, 1, ..., 7. All counters are reset before each kernel launch
(note that when collecting counters, kernel launches are synchronous as mentioned in
Concurrent Execution between Host and Device).
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B.18.Assertion
Assertion is only supported by devices of compute capability 2.x and higher. It is not
supported on MacOS, regardless of the device, and loading a module that references the
assert function on Mac OS will fail.
void assert(int expression);
stops the kernel execution if expression is equal to zero. If the program is run within a
debugger, this triggers a breakpoint and the debugger can be used to inspect the current
state of the device. Otherwise, each thread for which expression is equal to zero prints
a message to stderr after synchronization with the host via cudaDeviceSynchronize(),
cudaStreamSynchronize(), or cudaEventSynchronize(). The format of this
message is as follows:
<filename>:<line number>:<function>:
block: [blockId.x,blockId.x,blockIdx.z],
thread: [threadIdx.x,threadIdx.y,threadIdx.z]
Assertion `<expression>` failed.
Any subsequent host-side synchronization calls made for the same device will
return cudaErrorAssert. No more commands can be sent to this device until
cudaDeviceReset() is called to reinitialize the device.
If expression is different from zero, the kernel execution is unaffected.
For example, the following program from source file test.cu
#include <assert.h>
__global__ void testAssert(void)
{
int is_one = 1;
int should_be_one = 0;
// This will have no effect
assert(is_one);
// This will halt kernel execution
assert(should_be_one);
}
int main(int argc, char* argv[])
{
testAssert<<<1,1>>>();
cudaDeviceSynchronize();
return 0;
}
will output:
test.cu:19: void testAssert(): block: [0,0,0], thread: [0,0,0] Assertion
`should_be_one` failed.
Assertions are for debugging purposes. They can affect performance and it is therefore
recommended to disable them in production code. They can be disabled at compile
time by defining the NDEBUG preprocessor macro before including assert.h. Note that
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expression should not be an expression with side effects (something like (++i > 0),
for example), otherwise disabling the assertion will affect the functionality of the code.
B.19.Formatted Output
Formatted output is only supported by devices of compute capability 2.x and higher.
int printf(const char *format[, arg, ...]);
prints formatted output from a kernel to a host-side output stream.
The in-kernel printf() function behaves in a similar way to the standard C-library
printf() function, and the user is referred to the host system's manual pages for a
complete description of printf() behavior. In essence, the string passed in as format is
output to a stream on the host, with substitutions made from the argument list wherever
a format specifier is encountered. Supported format specifiers are listed below.
The printf() command is executed as any other device-side function: per-thread, and
in the context of the calling thread. From a multi-threaded kernel, this means that a
straightforward call to printf() will be executed by every thread, using that thread's
data as specified. Multiple versions of the output string will then appear at the host
stream, once for each thread which encountered the printf().
It is up to the programmer to limit the output to a single thread if only a single output
string is desired (see Examples for an illustrative example).
Unlike the C-standard printf(), which returns the number of characters printed,
CUDA's printf() returns the number of arguments parsed. If no arguments follow the
format string, 0 is returned. If the format string is NULL, -1 is returned. If an internal
error occurs, -2 is returned.
B.19.1.Format Specifiers
As for standard printf(), format specifiers take the form: %[flags][width]
[.precision][size]type
The following fields are supported (see widely-available documentation for a complete
description of all behaviors):
‣Flags: `#' ` ' `0' `+' `-'
‣Width: `*' `0-9'
‣Precision: `0-9'
‣Size: `h' `l' `ll'
‣Type: `%cdiouxXpeEfgGaAs'
Note that CUDA's printf()will accept any combination of flag, width, precision, size
and type, whether or not overall they form a valid format specifier. In other words, "%hd"
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will be accepted and printf will expect a double-precision variable in the corresponding
location in the argument list.
B.19.2.Limitations
Final formatting of the printf() output takes place on the host system. This means
that the format string must be understood by the host-system's compiler and C library.
Every effort has been made to ensure that the format specifiers supported by CUDA's
printf function form a universal subset from the most common host compilers, but exact
behavior will be host-OS-dependent.
As described in Format Specifiers, printf() will accept all combinations of valid flags
and types. This is because it cannot determine what will and will not be valid on the
host system where the final output is formatted. The effect of this is that output may be
undefined if the program emits a format string which contains invalid combinations.
The printf() command can accept at most 32 arguments in addition to the format
string. Additional arguments beyond this will be ignored, and the format specifier
output as-is.
Owing to the differing size of the long type on 64-bit Windows platforms (four bytes
on 64-bit Windows platforms, eight bytes on other 64-bit platforms), a kernel which is
compiled on a non-Windows 64-bit machine but then run on a win64 machine will see
corrupted output for all format strings which include "%ld". It is recommended that the
compilation platform matches the execution platform to ensure safety.
The output buffer for printf() is set to a fixed size before kernel launch (see
Associated Host-Side API). It is circular and if more output is produced during kernel
execution than can fit in the buffer, older output is overwritten. It is flushed only when
one of these actions is performed:
‣Kernel launch via <<<>>> or cuLaunchKernel() (at the start of the launch, and if
the CUDA_LAUNCH_BLOCKING environment variable is set to 1, at the end of the
launch as well),
‣Synchronization via cudaDeviceSynchronize(), cuCtxSynchronize(),
cudaStreamSynchronize(), cuStreamSynchronize(),
cudaEventSynchronize(), or cuEventSynchronize(),
‣Memory copies via any blocking version of cudaMemcpy*() or cuMemcpy*(),
‣Module loading/unloading via cuModuleLoad() or cuModuleUnload(),
‣Context destruction via cudaDeviceReset() or cuCtxDestroy().
‣Prior to executing a stream callback added by cudaStreamAddCallback or
cuStreamAddCallback.
Note that the buffer is not flushed automatically when the program exits. The user must
call cudaDeviceReset() or cuCtxDestroy() explicitly, as shown in the examples
below.
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Internally printf() uses a shared data structure and so it is possible that calling
printf() might change the order of execution of threads. In particular, a thread
which calls printf() might take a longer execution path than one which does not call
printf(), and that path length is dependent upon the parameters of the printf().
Note, however, that CUDA makes no guarantees of thread execution order except at
explicit __syncthreads() barriers, so it is impossible to tell whether execution order
has been modified by printf() or by other scheduling behaviour in the hardware.
B.19.3.Associated Host-Side API
The following API functions get and set the size of the buffer used to transfer the
printf() arguments and internal metadata to the host (default is 1 megabyte):
‣cudaDeviceGetLimit(size_t* size,cudaLimitPrintfFifoSize)
‣cudaDeviceSetLimit(cudaLimitPrintfFifoSize, size_t size)
B.19.4.Examples
The following code sample:
#include <stdio.h>
__global__ void helloCUDA(float f)
{
printf("Hello thread %d, f=%f\n", threadIdx.x, f);
}
int main()
{
helloCUDA<<<1, 5>>>(1.2345f);
cudaDeviceSynchronize();
return 0;
}
will output:
Hello thread 2, f=1.2345
Hello thread 1, f=1.2345
Hello thread 4, f=1.2345
Hello thread 0, f=1.2345
Hello thread 3, f=1.2345
Notice how each thread encounters the printf() command, so there are as many lines
of output as there were threads launched in the grid. As expected, global values (i.e.,
float f) are common between all threads, and local values (i.e., threadIdx.x) are
distinct per-thread.
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The following code sample:
#include <stdio.h>
__global__ void helloCUDA(float f)
{
if (threadIdx.x == 0)
printf("Hello thread %d, f=%f\n", threadIdx.x, f) ;
}
int main()
{
helloCUDA<<<1, 5>>>(1.2345f);
cudaDeviceSynchronize();
return 0;
}
will output:
Hello thread 0, f=1.2345
Self-evidently, the if() statement limits which threads will call printf, so that only a
single line of output is seen.
B.20.Dynamic Global Memory Allocation and
Operations
Dynamic global memory allocation and operations are only supported by devices of
compute capability 2.x and higher.
void* malloc(size_t size);
void free(void* ptr);
allocate and free memory dynamically from a fixed-size heap in global memory.
void* memcpy(void* dest, const void* src, size_t size);
copy size bytes from the memory location pointed by src to the memory location
pointed by dest.
void* memset(void* ptr, int value, size_t size);
set size bytes of memory block pointed by ptr to value (interpreted as an unsigned
char).
The CUDA in-kernel malloc() function allocates at least size bytes from the device
heap and returns a pointer to the allocated memory or NULL if insufficient memory
exists to fulfill the request. The returned pointer is guaranteed to be aligned to a 16-byte
boundary.
The CUDA in-kernel free() function deallocates the memory pointed to by ptr, which
must have been returned by a previous call to malloc(). If ptr is NULL, the call to
free() is ignored. Repeated calls to free() with the same ptr has undefined behavior.
The memory allocated by a given CUDA thread via malloc() remains allocated for the
lifetime of the CUDA context, or until it is explicitly released by a call to free(). It can
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be used by any other CUDA threads even from subsequent kernel launches. Any CUDA
thread may free memory allocated by another thread, but care should be taken to ensure
that the same pointer is not freed more than once.
B.20.1.Heap Memory Allocation
The device memory heap has a fixed size that must be specified before any program
using malloc() or free() is loaded into the context. A default heap of eight megabytes
is allocated if any program uses malloc() without explicitly specifying the heap size.
The following API functions get and set the heap size:
‣cudaDeviceGetLimit(size_t* size, cudaLimitMallocHeapSize)
‣cudaDeviceSetLimit(cudaLimitMallocHeapSize, size_t size)
The heap size granted will be at least size bytes. cuCtxGetLimit()and
cudaDeviceGetLimit() return the currently requested heap size.
The actual memory allocation for the heap occurs when a module is loaded into the
context, either explicitly via the CUDA driver API (see Module), or implicitly via the
CUDA runtime API (see CUDA C Runtime). If the memory allocation fails, the module
load will generate a CUDA_ERROR_SHARED_OBJECT_INIT_FAILED error.
Heap size cannot be changed once a module load has occurred and it does not resize
dynamically according to need.
Memory reserved for the device heap is in addition to memory allocated through host-
side CUDA API calls such as cudaMalloc().
B.20.2.Interoperability with Host Memory API
Memory allocated via device malloc() cannot be freed using the runtime (i.e., by
calling any of the free memory functions from Device Memory).
Similarly, memory allocated via the runtime (i.e., by calling any of the memory
allocation functions from Device Memory) cannot be freed via free().
In addition, device malloc() memory cannot be used in any runtime or driver API calls
(i.e. cudaMemcpy, cudaMemset, etc).
B.20.3.Examples
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B.20.3.1.Per Thread Allocation
The following code sample:
#include <stdlib.h>
#include <stdio.h>
__global__ void mallocTest()
{
size_t size = 123;
char* ptr = (char*)malloc(size);
memset(ptr, 0, size);
printf("Thread %d got pointer: %p\n", threadIdx.x, ptr);
free(ptr);
}
int main()
{
// Set a heap size of 128 megabytes. Note that this must
// be done before any kernel is launched.
cudaDeviceSetLimit(cudaLimitMallocHeapSize, 128*1024*1024);
mallocTest<<<1, 5>>>();
cudaDeviceSynchronize();
return 0;
}
will output:
Thread 0 got pointer: 00057020
Thread 1 got pointer: 0005708c
Thread 2 got pointer: 000570f8
Thread 3 got pointer: 00057164
Thread 4 got pointer: 000571d0
Notice how each thread encounters the malloc() and memset() commands and so
receives and initializes its own allocation. (Exact pointer values will vary: these are
illustrative.)
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B.20.3.2.Per Thread Block Allocation
#include <stdlib.h>
__global__ void mallocTest()
{
__shared__ int* data;
// The first thread in the block does the allocation and then
// shares the pointer with all other threads through shared memory,
// so that access can easily be coalesced.
// 64 bytes per thread are allocated.
if (threadIdx.x == 0) {
size_t size = blockDim.x * 64;
data = (int*)malloc(size);
}
__syncthreads();
// Check for failure
if (data == NULL)
return;
// Threads index into the memory, ensuring coalescence
int* ptr = data;
for (int i = 0; i < 64; ++i)
ptr[i * blockDim.x + threadIdx.x] = threadIdx.x;
// Ensure all threads complete before freeing
__syncthreads();
// Only one thread may free the memory!
if (threadIdx.x == 0)
free(data);
}
int main()
{
cudaDeviceSetLimit(cudaLimitMallocHeapSize, 128*1024*1024);
mallocTest<<<10, 128>>>();
cudaDeviceSynchronize();
return 0;
}
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B.20.3.3.Allocation Persisting Between Kernel Launches
#include <stdlib.h>
#include <stdio.h>
#define NUM_BLOCKS 20
__device__ int* dataptr[NUM_BLOCKS]; // Per-block pointer
__global__ void allocmem()
{
// Only the first thread in the block does the allocation
// since we want only one allocation per block.
if (threadIdx.x == 0)
dataptr[blockIdx.x] = (int*)malloc(blockDim.x * 4);
__syncthreads();
// Check for failure
if (dataptr[blockIdx.x] == NULL)
return;
// Zero the data with all threads in parallel
dataptr[blockIdx.x][threadIdx.x] = 0;
}
// Simple example: store thread ID into each element
__global__ void usemem()
{
int* ptr = dataptr[blockIdx.x];
if (ptr != NULL)
ptr[threadIdx.x] += threadIdx.x;
}
// Print the content of the buffer before freeing it
__global__ void freemem()
{
int* ptr = dataptr[blockIdx.x];
if (ptr != NULL)
printf("Block %d, Thread %d: final value = %d\n",
blockIdx.x, threadIdx.x, ptr[threadIdx.x]);
// Only free from one thread!
if (threadIdx.x == 0)
free(ptr);
}
int main()
{
cudaDeviceSetLimit(cudaLimitMallocHeapSize, 128*1024*1024);
// Allocate memory
allocmem<<< NUM_BLOCKS, 10 >>>();
// Use memory
usemem<<< NUM_BLOCKS, 10 >>>();
usemem<<< NUM_BLOCKS, 10 >>>();
usemem<<< NUM_BLOCKS, 10 >>>();
// Free memory
freemem<<< NUM_BLOCKS, 10 >>>();
cudaDeviceSynchronize();
return 0;
}
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B.21.Execution Configuration
Any call to a __global__ function must specify the execution configuration for that call.
The execution configuration defines the dimension of the grid and blocks that will be
used to execute the function on the device, as well as the associated stream (see CUDA C
Runtime for a description of streams).
The execution configuration is specified by inserting an expression of the form <<<
Dg, Db, Ns, S >>> between the function name and the parenthesized argument list,
where:
‣Dg is of type dim3 (see dim3) and specifies the dimension and size of the grid, such
that Dg.x * Dg.y * Dg.z equals the number of blocks being launched;
‣Db is of type dim3 (see dim3) and specifies the dimension and size of each block,
such that Db.x * Db.y * Db.z equals the number of threads per block;
‣Ns is of type size_t and specifies the number of bytes in shared memory that is
dynamically allocated per block for this call in addition to the statically allocated
memory; this dynamically allocated memory is used by any of the variables
declared as an external array as mentioned in __shared__; Ns is an optional
argument which defaults to 0;
‣S is of type cudaStream_t and specifies the associated stream; S is an optional
argument which defaults to 0.
As an example, a function declared as
__global__ void Func(float* parameter);
must be called like this:
Func<<< Dg, Db, Ns >>>(parameter);
The arguments to the execution configuration are evaluated before the actual function
arguments.
The function call will fail if Dg or Db are greater than the maximum sizes allowed for
the device as specified in Compute Capabilities, or if Ns is greater than the maximum
amount of shared memory available on the device, minus the amount of shared memory
required for static allocation.
B.22.Launch Bounds
As discussed in detail in Multiprocessor Level, the fewer registers a kernel uses, the
more threads and thread blocks are likely to reside on a multiprocessor, which can
improve performance.
Therefore, the compiler uses heuristics to minimize register usage while keeping
register spilling (see Device Memory Accesses) and instruction count to a minimum.
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An application can optionally aid these heuristics by providing additional
information to the compiler in the form of launch bounds that are specified using the
__launch_bounds__() qualifier in the definition of a __global__ function:
__global__ void
__launch_bounds__(maxThreadsPerBlock, minBlocksPerMultiprocessor)
MyKernel(...)
{
...
}
‣maxThreadsPerBlock specifies the maximum number of threads per block with
which the application will ever launch MyKernel(); it compiles to the .maxntid
PTX directive;
‣minBlocksPerMultiprocessor is optional and specifies the desired minimum
number of resident blocks per multiprocessor; it compiles to the .minnctapersm
PTX directive.
If launch bounds are specified, the compiler first derives from them
the upper limit L on the number of registers the kernel should use to
ensure that minBlocksPerMultiprocessor blocks (or a single block if
minBlocksPerMultiprocessor is not specified) of maxThreadsPerBlock threads
can reside on the multiprocessor (see Hardware Multithreading for the relationship
between the number of registers used by a kernel and the number of registers allocated
per block). The compiler then optimizes register usage in the following way:
‣If the initial register usage is higher than L, the compiler reduces it further until it
becomes less or equal to L, usually at the expense of more local memory usage and/
or higher number of instructions;
‣If the initial register usage is lower than L
‣If maxThreadsPerBlock is specified and minBlocksPerMultiprocessor is
not, the compiler uses maxThreadsPerBlock to determine the register usage
thresholds for the transitions between n and n+1 resident blocks (i.e., when
using one less register makes room for an additional resident block as in the
example of Multiprocessor Level) and then applies similar heuristics as when no
launch bounds are specified;
‣If both minBlocksPerMultiprocessor and maxThreadsPerBlock are
specified, the compiler may increase register usage as high as L to reduce the
number of instructions and better hide single thread instruction latency.
A kernel will fail to launch if it is executed with more threads per block than its launch
bound maxThreadsPerBlock.
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Optimal launch bounds for a given kernel will usually differ across major architecture
revisions. The sample code below shows how this is typically handled in device code
using the __CUDA_ARCH__ macro introduced in Application Compatibility
#define THREADS_PER_BLOCK 256
#if __CUDA_ARCH__ >= 200
#define MY_KERNEL_MAX_THREADS (2 * THREADS_PER_BLOCK)
#define MY_KERNEL_MIN_BLOCKS 3
#else
#define MY_KERNEL_MAX_THREADS THREADS_PER_BLOCK
#define MY_KERNEL_MIN_BLOCKS 2
#endif
// Device code
__global__ void
__launch_bounds__(MY_KERNEL_MAX_THREADS, MY_KERNEL_MIN_BLOCKS)
MyKernel(...)
{
...
}
In the common case where MyKernel is invoked with the maximum number of threads
per block (specified as the first parameter of __launch_bounds__()), it is tempting
to use MY_KERNEL_MAX_THREADS as the number of threads per block in the execution
configuration:
// Host code
MyKernel<<<blocksPerGrid, MY_KERNEL_MAX_THREADS>>>(...);
This will not work however since __CUDA_ARCH__ is undefined in host code as
mentioned in Application Compatibility, so MyKernel will launch with 256 threads
per block even when __CUDA_ARCH__ is greater or equal to 200. Instead the number of
threads per block should be determined:
‣Either at compile time using a macro that does not depend on __CUDA_ARCH__, for
example
// Host code
MyKernel<<<blocksPerGrid, THREADS_PER_BLOCK>>>(...);
‣Or at runtime based on the compute capability
// Host code
cudaGetDeviceProperties(&deviceProp, device);
int threadsPerBlock =
(deviceProp.major >= 2 ?
2 * THREADS_PER_BLOCK : THREADS_PER_BLOCK);
MyKernel<<<blocksPerGrid, threadsPerBlock>>>(...);
Register usage is reported by the --ptxas options=-v compiler option. The number
of resident blocks can be derived from the occupancy reported by the CUDA profiler
(see Device Memory Accessesfor a definition of occupancy).
Register usage can also be controlled for all __global__ functions in a file using the
maxrregcount compiler option. The value of maxrregcount is ignored for functions
with launch bounds.
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B.23.#pragma unroll
By default, the compiler unrolls small loops with a known trip count. The #pragma
unroll directive however can be used to control unrolling of any given loop. It must
be placed immediately before the loop and only applies to that loop. It is optionally
followed by an integral constant expression (ICE)6. If the ICE is absent, the loop will be
completely unrolled if its trip count is constant. If the ICE evaluates to 1, the compiler
will not unroll the loop. The pragma will be ignored if the ICE evaluates to a non-
positive integer or to an integer greater than the maximum value representable by the
int data type.
Examples:
struct S1_t { static const int value = 4; };
template <int X, typename T2>
__device__ void foo(int *p1, int *p2) {
// no argument specified, loop will be completely unrolled
#pragma unroll
for (int i = 0; i < 12; ++i)
p1[i] += p2[i]*2;
// unroll value = 8
#pragma unroll (X+1)
for (int i = 0; i < 12; ++i)
p1[i] += p2[i]*4;
// unroll value = 1, loop unrolling disabled
#pragma unroll 1
for (int i = 0; i < 12; ++i)
p1[i] += p2[i]*8;
// unroll value = 4
#pragma unroll (T2::value)
for (int i = 0; i < 12; ++i)
p1[i] += p2[i]*16;
}
__global__ void bar(int *p1, int *p2) {
foo<7, S1_t>(p1, p2);
}
B.24.SIMD Video Instructions
PTX ISA version 3.0 includes SIMD (Single Instruction, Multiple Data) video instructions
which operate on pairs of 16-bit values and quads of 8-bit values. These are available on
devices of compute capability 3.0.
The SIMD video instructions are:
‣vadd2, vadd4
‣vsub2, vsub4
6See the C++ Standard for definition of integral constant expression.
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‣vavrg2, vavrg4
‣vabsdiff2, vabsdiff4
‣vmin2, vmin4
‣vmax2, vmax4
‣vset2, vset4
PTX instructions, such as the SIMD video instructions, can be included in CUDA
programs by way of the assembler, asm(), statement.
The basic syntax of an asm() statement is:
asm("template-string" : "constraint"(output) : "constraint"(input)"));
An example of using the vabsdiff4 PTX instruction is:
asm("vabsdiff4.u32.u32.u32.add" " %0, %1, %2, %3;": "=r" (result):"r" (A), "r"
(B), "r" (C));
This uses the vabsdiff4 instruction to compute an integer quad byte SIMD sum of
absolute differences. The absolute difference value is computed for each byte of the
unsigned integers A and B in SIMD fashion. The optional accumulate operation (.add)
is specified to sum these differences.
Refer to the document "Using Inline PTX Assembly in CUDA" for details on using
the assembly statement in your code. Refer to the PTX ISA documentation ("Parallel
Thread Execution ISA Version 3.0" for example) for details on the PTX instructions for
the version of PTX that you are using.
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AppendixC.
COOPERATIVE GROUPS
C.1.Introduction
Cooperative Groups is an extension to the CUDA programming model, introduced in
CUDA 9, for organizing groups of communicating threads. Cooperative Groups allows
developers to express the granularity at which threads are communicating, helping them
to express richer, more efficient parallel decompositions.
Historically, the CUDA programming model has provided a single, simple construct
for synchronizing cooperating threads: a barrier across all threads of a thread block, as
implemented with the __syncthreads() intrinsic function. However, programmers
would like to define and synchronize groups of threads at other granularities to enable
greater performance, design flexibility, and software reuse in the form of “collective”
group-wide function interfaces. In an effort to express broader patterns of parallel
interaction, many performance-oriented programmers have resorted to writing their
own ad hoc and unsafe primitives for synchronizing threads within a single warp,
or across sets of thread blocks running on a single GPU. Whilst the performance
improvements achieved have often been valuable, this has resulted in an ever-growing
collection of brittle code that is expensive to write, tune, and maintain over time and
across GPU generations. Cooperative Groups addresses this by providing a safe and
future-proof mechanism to enable performant code.
The Cooperative Groups programming model extension describes synchronization
patterns both within and across CUDA thread blocks. It provides both the means for
applications to define their own groups of threads, and the interfaces to synchronize
them. It also provides new launch APIs that enforce certain restrictions and therefore
can guarantee the synchronization will work. These primitives enable new patterns
of cooperative parallelism within CUDA, including producer-consumer parallelism,
opportunistic parallelism, and global synchronization across the entire Grid.
The expression of groups as first-class program objects improves software composition,
since collective functions can receive an explicit object representing the group of
participating threads. This object also makes programmer intent explicit, which
eliminates unsound architectural assumptions that result in brittle code, undesirable
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restrictions upon compiler optimizations, and better compatibility with new GPU
generations.
The Cooperative Groups programming model consists of the following elements:
‣data types for representing groups of cooperating threads;
‣operations to obtain intrinsic groups defined by the CUDA launch API (e.g., thread
blocks);
‣operations for partitioning existing groups into new groups;
‣a barrier operation to synchronize a given group;
‣and operations to inspect the group properties as well as group-specific collectives.
C.2.Intra-block Groups
In this section we describe the functionality available to create groups of threads within
a thread block that can synchronize and collaborate. Note that the use of Cooperative
Groups for synchronization across thread blocks or devices requires some additional
considerations, as described later in this appendix.
Cooperative Groups requires CUDA 9.0 or later. To use Cooperative Groups, include the
header file:
#include <cooperative_groups.h>
and use the Cooperative Groups namespace:
using namespace cooperative_groups;
Then code containing any intra-block Cooperative Groups functionality can be compiled
in the normal way using nvcc.
C.2.1.Thread Groups and Thread Blocks
Any CUDA programmer is already familiar with a certain group of threads: the thread
block. The Cooperative Groups extension introduces a new datatype, thread_block,
to explicitly represent this concept within the kernel. The group can be initialized as
follows:
thread_block g = this_thread_block();
The thread_block datatype is derived from the more generic thread_group datatype,
which can be used to represent a wider class of groups. thread_group provides the
following functionality:
void sync(); // Synchronize the threads in the group
unsigned size(); // Total number of threads in the group
unsigned thread_rank(); // Rank of the calling thread within [0, size]
bool is_valid(); // Whether the group violated any APIconstraints
whereas thread_block provides the following additional block-specific functionality:
dim3 group_index(); // 3-dimensional block index within the grid
dim3 thread_index(); // 3-dimensional thread index within the block
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For example, if the group g is initialized as above, then
g.sync();
will synchronize all threads in the block (i.e. equivalent to __syncthreads();).
Note that all threads in the group must participate in collective operations, or the
behavior is undefined.
C.2.2.Tiled Partitions
The tiled_partition() function can be used to decompose the thread block into
multiple smaller groups of cooperative threads. For example, if we first create a group
containing all the threads in the block:
thread_block wholeBlock = this_thread_block();
then we can partition this into smaller groups, each of size 32 threads:
thread_group tile32 = tiled_partition(wholeBlock, 32);
and, furthermore, we can partition each of these groups into even smaller groups, each
of size 4 threads:
thread_group tile4 = tiled_partition(tile32, 4);
If, for instance, if we were to then include the following line of code:
if (tile4.thread_rank()==0) printf(“Hello from tile4 rank 0\n”);
then the statement would be printed by every fourth thread in the block: the threads of
rank 0 in each tile4 group, which correspond to those threads with ranks 0,4,8,12… in
the wholeBlock group.
Note that, currently, only supported are tile sizes which are a power of 2 and no larger
than 32.
C.2.3.Thread Block Tiles
An alternative templated version of the tiled_partition function is available,
where a template parameter is used to specify the size of the tile: with this known at
compile time there is the potential for more optimal execution. Analogous to that in the
previous section, the following code will create two sets of tiled groups, of size 32 and 4
respectively:
thread_block_tile<32> tile32 = tiled_partition<32>(this_thread_block());
thread_block_tile<4> tile4 = tiled_partition<4>(this_thread_block());
Note that the thread_block_tile templated data structure is being used here,
and that the size of the group is passed to the tiled_partition call as a template
parameter rather than an argument.
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Thread Block Tiles also expose additional functionality as follows:
.shfl()
.shfl_down()
.shfl_up()
.shfl_xor()
.any()
.all()
.ballot()
.match_any()
.match_all()
where these cooperative synchronous operations are analogous to those described in
Warp Shuffle Functions and Warp Vote Functions. However their use here, in the context
of these user-defined Cooperative Groups, offers enhanced flexibility and productivity.
This functionality will be demonstrated later in this appendix.
As mentioned above, only supported are tile sizes which are a power of 2 and no larger
than 32.
C.2.4.Coalesced Groups
In CUDA’s SIMT architecture, at the hardware level the multiprocessor executes threads
in groups of 32 called warps. If there exists a data-dependent conditional branch in
the application code such that threads within a warp diverge, then the warp serially
executes each branch disabling threads not on that path. The threads that remain
active on the path are referred to as coalesced. Cooperative Groups has functionality to
discover, and create, a group containing all coalesced threads as follows:
coalesced_group active = coalesced_threads();
For example, consider a situation whereby there is a branch in the code in which only
the 2nd, 4th and 8th threads in each warp are active. The above call, placed in that
branch, will create (for each warp) a group, active, that has three threads (with ranks
0-2 inclusive).
C.2.5.Uses of Intra-block Cooperative Groups
In this section, Cooperative Group functionality is illustrated through some usage
examples.
C.2.5.1.Discovery Pattern
Commonly developers need to work with the current active set of threads. No
assumption is made about the threads that are present, and instead developers work
with the threads that happen to be there. This is seen in the following “aggregating
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atomic increment across threads in a warp” example (written using the correct CUDA
9.0 set of intrinsics):
{
unsigned int writemask = __activemask();
unsigned int total = __popc(writemask);
unsigned int prefix = __popc(writemask & __lanemask_lt());
// Find the lowest-numbered active lane
int elected_lane = __ffs(writemask) - 1;
int base_offset = 0;
if (prefix == 0) {
base_offset = atomicAdd(p, total);
}
base_offset = __shfl_sync(writemask, base_offset, elected_lane);
int thread_offset = prefix + base_offset;
return thread_offset;
}
This can be re-written with Cooperative Groups as follows:
{
cg::coalesced_group g = cg::coalesced_threads();
int prev;
if (g.thread_rank() == 0) {
prev = atomicAdd(p, g.size());
}
prev = g.thread_rank() + g.shfl(prev, 0);
return prev;
}
C.2.5.2.Warp-Synchronous Code Pattern
Developers might have had warp-synchronous codes that they previously made implicit
assumptions about the warp size and would code around that number. Now this needs
to be specified explicitly.
// If the size is known statically
auto g = tiled_partition<16>(this_thread_block());
// Can use g.shfl and all other warp-synchronous builtins
However, the user might want to better partition his algorithm, but without needing the
advantage of warp-synchronous builtins.
auto g = tiled_partition(this_thread_block(), 8);
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In this case, the group g can still synchronize and you can still build varied parallel
algorithms on top, but shfl() etc. are not accessible.
__global__ void cooperative_kernel(...) {
// obtain default "current thread block" group
thread_group my_block = this_thread_block();
// subdivide into 32-thread, tiled subgroups
// Tiled subgroups evenly partition a parent group into
// adjacent sets of threads - in this case each one warp in size
thread_group my_tile = tiled_partition(my_block, 32);
// This operation will be performed by only the
// first 32-thread tile of each block
if (my_block.thread_rank() < 32) {
// ...
my_tile.sync();
}
}
C.2.5.3.Composition
Previously, there were hidden constraints on the implementation when writing certain
code. Take this example:
device__ int sum(int *x, int n) {
// ...
__syncthreads();
return total;
}
__global__ void parallel_kernel(float *x){
// ...
// Entire thread block must call sum
sum(x, n);
}
All threads in the thread block must arrive at the __syncthreads() barrier, however,
this constraint is hidden from the developer who might want to use sum(…). With
Cooperative Groups, a better way of writing this would be:
__device__ int sum(const thread_group& g, int *x, int n)
{
// ...
g.sync()
return total;
}
__global__ void parallel_kernel(...)
{
// ...
// Entire thread block must call sum
sum(this_thread_block(), x, n);
// ...
}
C.3.Grid Synchronization
Prior to the introduction of Cooperative Groups, the CUDA programming model only
allowed synchronization between thread blocks at a kernel completion boundary. The
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kernel boundary carries with it an implicit invalidation of state, and with it, potential
performance implications.
For example, in certain use cases, applications have a large number of small kernels,
with each kernel representing a stage in a processing pipeline. The presence of these
kernels is required by the current CUDA programming model to ensure that the thread
blocks operating on one pipeline stage have produced data before the thread block
operating on the next pipeline stage is ready to consume it. In such cases, the ability
to provide global inter thread block synchronization would allow the application to
be restructured to have persistent thread blocks, which are able to synchronize on the
device when a given stage is complete.
To synchronize across the grid, from within a kernel, you would simply use the group:
grid_group grid = this_grid();
and call:
grid.sync();
To enable grid synchronization, when launching the kernel it is necessary to use, instead
of the <<<...>>> execution configuration syntax, the cuLaunchCooperativeKernel
CUDA runtime launch API:
cudaLaunchCooperativeKernel(
const T *func,
dim3 gridDim,
dim3 blockDim,
void **args,
size_t sharedMem = 0,
cudaStream_t stream = 0
)
(or the CUDA driver equivalent).
To guarantee co-residency of the thread blocks on the GPU, the number of blocks
launched needs to be carefully considered. For example, a block per SM can be launched
as follows:
cudaDeviceProp deviceProp;
cudaGetDeviceProperties(&deviceProp, dev);
// initialize, then launch
cudaLaunchCooperativeKernel((void*)my_kernel, deviceProp.multiProcessorCount,
numThreads, args);
Alternatively, you can calculate how many blocks can fit simultaneously per-SM using
the occupancy calculator as follows:
cudaOccupancyMaxActiveBlocksPerMultiprocessor(&numBlocksPerSm, my_kernel,
numThreads, 0));
// initialize, then launch
cudaLaunchCooperativeKernel((void*)my_kernel, numBlocksPerSm, numThreads, args);
Note also that to use grid synchronization, the device code must be compiled in separate
compilation (see the "Using Separate Compilation in CUDA" section in the CUDA
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Compiler Driver NVCC documentation) and the device runtime linked in. The simplest
example is:
nvcc -arch=sm_61 -rdc=true mytestfile.cu -o mytest
You should also ensure the device supports the cooperative launch property, as can be
determined by usage of the cuDeviceAttribute CUDA driver API :
int pi=0;
cuDevice dev;
cuDeviceGet(&dev,0) // get handle to device 0
cuDeviceGetAttribute(&pi, CU_DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH, dev);
which will set pi to 1 if the property is supported on device 0.
C.4.Multi-Device Synchronization
In order to enable synchronization across multiple devices with Cooperative Groups,
use of the cuLaunchCooperativeKernelMultiDevice CUDA API is required. This, a
significant departure from existing CUDA APIs, will allow a single host thread to launch
a kernel across multiple devices. In addition to the constraints and guarantees made by
cuLaunchCooperativeKernel, this API has the additional semantics:
‣This API will ensure that a launch is atomic, i.e. if the API call succeeds, then the
provided number of thread blocks will launch on all specified devices.
‣The functions launched via this API must be identical. No explicit checks are
done by the driver in this regard because it is largely not feasible. It is up to the
application to ensure this.
‣No two entries in the provided launchParamsList may map to the same device.
‣All devices being targeted by this launch must be identical. i.e. they must have the
same major and minor number.
‣The block size, grid size and amount of shared memory per grid must be the same
across all devices. Note that this means the maximum number of blocks that can be
launched per device will be limited by the device with the least number of SMs.
‣Any user defined __device__, __constant__ or __managed__ device global
variables present in the module that owns the CUfunction being launched are
independently instantiated on every device. The user is responsible for initializing
such device global variables appropriately.
The launch parameters should be defined using a struct:
typedef struct CUDA_LAUNCH_PARAMS_st {
CUfunction function;
unsigned int gridDimX;
unsigned int gridDimY;
unsigned int gridDimZ;
unsigned int blockDimX;
unsigned int blockDimY;
unsigned int blockDimZ;
unsigned int sharedMemBytes;
CUstream hStream;
void **kernelParams;
} CUDA_LAUNCH_PARAMS;
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and passed into the launch API:
cudaLaunchCooperativeKernelMultiDevice(
CUDA_LAUNCH_PARAMS *launchParamsList,
unsigned int numDevices);
in a similar fashion to that for grid-wide synchronization described above. Also, as with
grid-wide synchronization, the resulting device code looks very similar:
multi_grid_group multi_grid = this_multi_grid();
multi_grid.sync();
and needs to be compiled in separate compilation.
You should also ensure the device supports the cooperative multi device launch
property in a similar way to that described in the previous section, but with use
of CU_DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH instead of
CU_DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH.
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AppendixD.
CUDA DYNAMIC PARALLELISM
D.1.Introduction
D.1.1.Overview
Dynamic Parallelism is an extension to the CUDA programming model enabling a CUDA
kernel to create and synchronize with new work directly on the GPU. The creation of
parallelism dynamically at whichever point in a program that it is needed offers exciting
new capabilities.
The ability to create work directly from the GPU can reduce the need to transfer
execution control and data between host and device, as launch configuration decisions
can now be made at runtime by threads executing on the device. Additionally,
data-dependent parallel work can be generated inline within a kernel at run-time,
taking advantage of the GPU's hardware schedulers and load balancers dynamically
and adapting in response to data-driven decisions or workloads. Algorithms and
programming patterns that had previously required modifications to eliminate
recursion, irregular loop structure, or other constructs that do not fit a flat, single-level of
parallelism may more transparently be expressed.
This document describes the extended capabilities of CUDA which enable Dynamic
Parallelism, including the modifications and additions to the CUDA programming
model necessary to take advantage of these, as well as guidelines and best practices for
exploiting this added capacity.
Dynamic Parallelism is only supported by devices of compute capability 3.5 and higher.
D.1.2.Glossary
Definitions for terms used in this guide.
Grid
A Grid is a collection of Threads. Threads in a Grid execute a Kernel Function and are
divided into Thread Blocks.
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Thread Block
A Thread Block is a group of threads which execute on the same multiprocessor
(SMX). Threads within a Thread Block have access to shared memory and can be
explicitly synchronized.
Kernel Function
A Kernel Function is an implicitly parallel subroutine that executes under the CUDA
execution and memory model for every Thread in a Grid.
Host
The Host refers to the execution environment that initially invoked CUDA. Typically
the thread running on a system's CPU processor.
Parent
A Parent Thread, Thread Block, or Grid is one that has launched new grid(s), the Child
Grid(s). The Parent is not considered completed until all of its launched Child Grids
have also completed.
Child
A Child thread, block, or grid is one that has been launched by a Parent grid. A Child
grid must complete before the Parent Thread, Thread Block, or Grid are considered
complete.
Thread Block Scope
Objects with Thread Block Scope have the lifetime of a single Thread Block. They only
have defined behavior when operated on by Threads in the Thread Block that created
the object and are destroyed when the Thread Block that created them is complete.
Device Runtime
The Device Runtime refers to the runtime system and APIs available to enable Kernel
Functions to use Dynamic Parallelism.
D.2.Execution Environment and Memory Model
D.2.1.Execution Environment
The CUDA execution model is based on primitives of threads, thread blocks, and
grids, with kernel functions defining the program executed by individual threads
within a thread block and grid. When a kernel function is invoked the grid's properties
are described by an execution configuration, which has a special syntax in CUDA.
Support for dynamic parallelism in CUDA extends the ability to configure, launch, and
synchronize upon new grids to threads that are running on the device.
D.2.1.1.Parent and Child Grids
A device thread that configures and launches a new grid belongs to the parent grid, and
the grid created by the invocation is a child grid.
The invocation and completion of child grids is properly nested, meaning that the
parent grid is not considered complete until all child grids created by its threads have
completed. Even if the invoking threads do not explicitly synchronize on the child grids
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launched, the runtime guarantees an implicit synchronization between the parent and
child.
Time
CPU Thread
Grid A - Parent
Grid B - Child
Grid A Threads
Grid B Threads
Grid A Launch
Grid B Launch
Grid A Complete
Grid B Complete
Figure12 Parent-Child Launch Nesting
D.2.1.2.Scope of CUDA Primitives
On both host and device, the CUDA runtime offers an API for launching kernels,
for waiting for launched work to complete, and for tracking dependencies between
launches via streams and events. On the host system, the state of launches and the
CUDA primitives referencing streams and events are shared by all threads within a
process; however processes execute independently and may not share CUDA objects.
A similar hierarchy exists on the device: launched kernels and CUDA objects are visible
to all threads in a thread block, but are independent between thread blocks. This means
for example that a stream may be created by one thread and used by any other thread in
the same thread block, but may not be shared with threads in any other thread block.
D.2.1.3.Synchronization
CUDA runtime operations from any thread, including kernel launches, are visible across
a thread block. This means that an invoking thread in the parent grid may perform
synchronization on the grids launched by that thread, by other threads in the thread
block, or on streams created within the same thread block. Execution of a thread block
is not considered complete until all launches by all threads in the block have completed.
If all threads in a block exit before all child launches have completed, a synchronization
operation will automatically be triggered.
D.2.1.4.Streams and Events
CUDA Streams and Events allow control over dependencies between grid launches:
grids launched into the same stream execute in-order, and events may be used to create
dependencies between streams. Streams and events created on the device serve this
exact same purpose.
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Streams and events created within a grid exist within thread block scope but have
undefined behavior when used outside of the thread block where they were created. As
described above, all work launched by a thread block is implicitly synchronized when
the block exits; work launched into streams is included in this, with all dependencies
resolved appropriately. The behavior of operations on a stream that has been modified
outside of thread block scope is undefined.
Streams and events created on the host have undefined behavior when used within any
kernel, just as streams and events created by a parent grid have undefined behavior if
used within a child grid.
D.2.1.5.Ordering and Concurrency
The ordering of kernel launches from the device runtime follows CUDA Stream
ordering semantics. Within a thread block, all kernel launches into the same stream are
executed in-order. With multiple threads in the same thread block launching into the
same stream, the ordering within the stream is dependent on the thread scheduling
within the block, which may be controlled with synchronization primitives such as
__syncthreads().
Note that because streams are shared by all threads within a thread block, the implicit
NULL stream is also shared. If multiple threads in a thread block launch into the implicit
stream, then these launches will be executed in-order. If concurrency is desired, explicit
named streams should be used.
Dynamic Parallelism enables concurrency to be expressed more easily within a program;
however, the device runtime introduces no new concurrency guarantees within the
CUDA execution model. There is no guarantee of concurrent execution between any
number of different thread blocks on a device.
The lack of concurrency guarantee extends to parent thread blocks and their child grids.
When a parent thread block launches a child grid, the child is not guaranteed to begin
execution until the parent thread block reaches an explicit synchronization point (e.g.
cudaDeviceSynchronize()).
While concurrency will often easily be achieved, it may vary as a function of
deviceconfiguration, application workload, and runtime scheduling. It is therefore
unsafe to depend upon any concurrency between different thread blocks.
D.2.1.6.Device Management
There is no multi-GPU support from the device runtime; the device runtime is only
capable of operating on the device upon which it is currently executing. It is permitted,
however, to query properties for any CUDA capable device in the system.
D.2.2.Memory Model
Parent and child grids share the same global and constant memory storage, but have
distinct local and shared memory.
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D.2.2.1.Coherence and Consistency
D.2.2.1.1.Global Memory
Parent and child grids have coherent access to global memory, with weak consistency
guarantees between child and parent. There are two points in the execution of a child
grid when its view of memory is fully consistent with the parent thread: when the
child grid is invoked by the parent, and when the child grid completes as signaled by a
synchronization API invocation in the parent thread.
All global memory operations in the parent thread prior to the child grid's invocation are
visible to the child grid. All memory operations of the child grid are visible to the parent
after the parent has synchronized on the child grid's completion.
In the following example, the child grid executing child_launch is only guaranteed
to see the modifications to data made before the child grid was launched. Since thread
0 of the parent is performing the launch, the child will be consistent with the memory
seen by thread 0 of the parent. Due to the first __syncthreads() call, the child will see
data[0]=0, data[1]=1, ..., data[255]=255 (without the __syncthreads() call, only
data[0] would be guaranteed to be seen by the child). When the child grid returns,
thread 0 is guaranteed to see modifications made by the threads in its child grid. Those
modifications become available to the other threads of the parent grid only after the
second __syncthreads() call:
__global__ void child_launch(int *data) {
data[threadIdx.x] = data[threadIdx.x]+1;
}
__global__ void parent_launch(int *data) {
data[threadIdx.x] = threadIdx.x;
__syncthreads();
if (threadIdx.x == 0) {
child_launch<<< 1, 256 >>>(data);
cudaDeviceSynchronize();
}
__syncthreads();
}
void host_launch(int *data) {
parent_launch<<< 1, 256 >>>(data);
}
D.2.2.1.2.Zero Copy Memory
Zero-copy system memory has identical coherence and consistency guarantees to global
memory, and follows the semantics detailed above. A kernel may not allocate or free
zero-copy memory, but may use pointers to zero-copy passed in from the host program.
D.2.2.1.3.Constant Memory
Constants are immutable and may not be modified from the device, even between
parent and child launches. That is to say, the value of all __constant__ variables must
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be set from the host prior to launch. Constant memory is inherited automatically by all
child kernels from their respective parents.
Taking the address of a constant memory object from within a kernel thread has the
same semantics as for all CUDA programs, and passing that pointer from parent to child
or from a child to parent is naturally supported.
D.2.2.1.4.Shared and Local Memory
Shared and Local memory is private to a thread block or thread, respectively, and is not
visible or coherent between parent and child. Behavior is undefined when an object in
one of these locations is referenced outside of the scope within which it belongs, and
may cause an error.
The NVIDIA compiler will attempt to warn if it can detect that a pointer to local or
shared memory is being passed as an argument to a kernel launch. At runtime, the
programmer may use the __isGlobal() intrinsic to determine whether a pointer
references global memory and so may safely be passed to a child launch.
Note that calls to cudaMemcpy*Async() or cudaMemset*Async() may invoke new
child kernels on the device in order to preserve stream semantics. As such, passing
shared or local memory pointers to these APIs is illegal and will return an error.
D.2.2.1.5.Local Memory
Local memory is private storage for an executing thread, and is not visible outside of
that thread. It is illegal to pass a pointer to local memory as a launch argument when
launching a child kernel. The result of dereferencing such a local memory pointer from a
child will be undefined.
For example the following is illegal, with undefined behavior if x_array is accessed by
child_launch:
int x_array[10]; // Creates x_array in parent's local memory
child_launch<<< 1, 1 >>>(x_array);
It is sometimes difficult for a programmer to be aware of when a variable is placed into
local memory by the compiler. As a general rule, all storage passed to a child kernel
should be allocated explicitly from the global-memory heap, either with cudaMalloc(),
new() or by declaring __device__ storage at global scope. For example:
// Correct - "value" is global storage
__device__ int value;
__device__ void x() {
value = 5;
child<<< 1, 1 >>>(&value);
}
// Invalid - "value" is local storage
__device__ void y() {
int value = 5;
child<<< 1, 1 >>>(&value);
}
D.2.2.1.6.Texture Memory
Writes to the global memory region over which a texture is mapped are incoherent with
respect to texture accesses. Coherence for texture memory is enforced at the invocation
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of a child grid and when a child grid completes. This means that writes to memory prior
to a child kernel launch are reflected in texture memory accesses of the child. Similarly,
writes to memory by a child will be reflected in the texture memory accesses by a parent,
but only after the parent synchronizes on the child's completion. Concurrent accesses by
parent and child may result in inconsistent data.
D.3.Programming Interface
D.3.1.CUDA C/C++ Reference
This section describes changes and additions to the CUDA C/C++ language extensions
for supporting Dynamic Parallelism.
The language interface and API available to CUDA kernels using CUDA C/C++ for
Dynamic Parallelism, referred to as the Device Runtime, is substantially like that of the
CUDA Runtime API available on the host. Where possible the syntax and semantics of
the CUDA Runtime API have been retained in order to facilitate ease of code reuse for
routines that may run in either the host or device environments.
As with all code in CUDA C/C++, the APIs and code outlined here is per-thread code.
This enables each thread to make unique, dynamic decisions regarding what kernel or
operation to execute next. There are no synchronization requirements between threads
within a block to execute any of the provided device runtime APIs, which enables the
device runtime API functions to be called in arbitrarily divergent kernel code without
deadlock.
D.3.1.1.Device-Side Kernel Launch
Kernels may be launched from the device using the standard CUDA <<< >>> syntax:
kernel_name<<< Dg, Db, Ns, S >>>([kernel arguments]);
‣Dg is of type dim3 and specifies the dimensions and size of the grid
‣Db is of type dim3 and specifies the dimensions and size of each thread block
‣Ns is of type size_t and specifies the number of bytes of shared memory that
is dynamically allocated per thread block for this call and addition to statically
allocated memory. Ns is an optional argument that defaults to 0.
‣S is of type cudaStream_t and specifies the stream associated with this call. The
stream must have been allocated in the same thread block where the call is being
made. S is an optional argument that defaults to 0.
D.3.1.1.1.Launches are Asynchronous
Identical to host-side launches, all device-side kernel launches are asynchronous with
respect to the launching thread. That is to say, the <<<>>> launch command will return
immediately and the launching thread will continue to execute until it hits an explicit
launch-synchronization point such as cudaDeviceSynchronize(). The grid launch is
posted to the device and will execute independently of the parent thread. The child grid
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may begin execution at any time after launch, but is not guaranteed to begin execution
until the launching thread reaches an explicit launch-synchronization point.
D.3.1.1.2.Launch Environment Configuration
All global device configuration settings (e.g., shared memory and L1 cache size as
returned from cudaDeviceGetCacheConfig(), and device limits returned from
cudaDeviceGetLimit()) will be inherited from the parent. That is to say if, when the
parent is launched, execution is configured globally for 16k of shared memory and 48k
of L1 cache, then the child's execution state will be configured identically. Likewise,
device limits such as stack size will remain as-configured.
For host-launched kernels, per-kernel configurations set from the host will take
precedence over the global setting. These configurations will be used when the kernel is
launched from the device as well. It is not possible to reconfigure a kernel's environment
from the device.
D.3.1.2.Streams
Both named and unnamed (NULL) streams are available from the device runtime.
Named streams may be used by any thread within a thread-block, but stream handles
may not be passed to other blocks or child/parent kernels. In other words, a stream
should be treated as private to the block in which it is created. Stream handles are not
guaranteed to be unique between blocks, so using a stream handle within a block that
did not allocate it will result in undefined behavior.
Similar to host-side launch, work launched into separate streams may run concurrently,
but actual concurrency is not guaranteed. Programs that depend upon concurrency
between child kernels are not supported by the CUDA programming model and will
have undefined behavior.
The host-side NULL stream's cross-stream barrier semantic is not supported on the
device (see below for details). In order to retain semantic compatibility with the host
runtime, all device streams must be created using the cudaStreamCreateWithFlags()
API, passing the cudaStreamNonBlocking flag. The cudaStreamCreate() call is a
host-runtime- only API and will fail to compile for the device.
As cudaStreamSynchronize() and cudaStreamQuery() are unsupported by
the device runtime, cudaDeviceSynchronize() should be used instead when the
application needs to know that stream-launched child kernels have completed.
D.3.1.2.1.The Implicit (NULL) Stream
Within a host program, the unnamed (NULL) stream has additional barrier
synchronization semantics with other streams (see Default Stream for details). The
device runtime offers a single implicit, unnamed stream shared between all threads in
a block, but as all named streams must be created with the cudaStreamNonBlocking
flag, work launched into the NULL stream will not insert an implicit dependency on
pending work in any other streams.
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D.3.1.3.Events
Only the inter-stream synchronization capabilities of CUDA events are
supported. This means that cudaStreamWaitEvent() is supported, but
cudaEventSynchronize(), cudaEventElapsedTime(), and cudaEventQuery() are
not. As cudaEventElapsedTime() is not supported, cudaEvents must be created via
cudaEventCreateWithFlags(), passing the cudaEventDisableTiming flag.
As for all device runtime objects, event objects may be shared between all threads
withinthe thread-block which created them but are local to that block and may not be
passed to other kernels, or between blocks within the same kernel. Event handles are not
guaranteed to be unique between blocks, so using an event handle within a block that
did not create it will result in undefined behavior.
D.3.1.4.Synchronization
The cudaDeviceSynchronize() function will synchronize on all work launched by
any thread in the thread-block up to the point where cudaDeviceSynchronize() was
called. Note that cudaDeviceSynchronize() may be called from within divergent
code (see Block Wide Synchronization).
It is up to the program to perform sufficient additional inter-thread synchronization, for
example via a call to __syncthreads(), if the calling thread is intended to synchronize
with child grids invoked from other threads.
D.3.1.4.1.Block Wide Synchronization
The cudaDeviceSynchronize() function does not imply intra-block synchronization.
In particular, without explicit synchronization via a __syncthreads() directive
the calling thread can make no assumptions about what work has been launched by
any thread other than itself. For example if multiple threads within a block are each
launching work and synchronization is desired for all this work at once (perhaps
because of event-based dependencies), it is up to the program to guarantee that this
work is submitted by all threads before calling cudaDeviceSynchronize().
Because the implementation is permitted to synchronize on launches from any thread in
the block, it is quite possible that simultaneous calls to cudaDeviceSynchronize() by
multiple threads will drain all work in the first call and then have no effect for the later
calls.
D.3.1.5.Device Management
Only the device on which a kernel is running will be controllable from that kernel.
This means that device APIs such as cudaSetDevice() are not supported by
the device runtime. The active device as seen from the GPU (returned from
cudaGetDevice()) will have the same device number as seen from the host system.
The cudaDeviceGetAttribute() call may request information about another device
as this API allows specification of a device ID as a parameter of the call. Note that the
catch-all cudaGetDeviceProperties() API is not offered by the device runtime -
properties must be queried individually.
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D.3.1.6.Memory Declarations
D.3.1.6.1.Device and Constant Memory
Memory declared at file scope with __device__ or __constant__ memory space
specifiers behaves identically when using the device runtime. All kernels may read
or write device variables, whether the kernel was initially launched by the host or
device runtime. Equivalently, all kernels will have the same view of __constant__s as
declared at the module scope.
D.3.1.6.2.Textures & Surfaces
CUDA supports dynamically created texture and surface objects1, where a texture
reference may be created on the host, passed to a kernel, used by that kernel, and then
destroyed from the host. The device runtime does not allow creation or destruction
of texture or surface objects from within device code, but texture and surface objects
created from the host may be used and passed around freely on the device. Regardless
of where they are created, dynamically created texture objects are always valid and may
be passed to child kernels from a parent.
The device runtime does not support legacy module-scope (i.e., Fermi-style) textures
and surfaces within a kernel launched from the device. Module-scope (legacy)
textures may be created from the host and used in device code as for any kernel,
but may only be used by a top-level kernel (i.e., the one which is launched from the
host).
D.3.1.6.3.Shared Memory Variable Declarations
In CUDA C/C++ shared memory can be declared either as a statically sized file-scope or
function-scoped variable, or as an extern variable with the size determined at runtime
by the kernel's caller via a launch configuration argument. Both types of declarations are
valid under the device runtime.
1Dynamically created texture and surface objects are an addition to the CUDA memory model introduced with CUDA
5.0. Please see the CUDA Programming Guide for details.
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__global__ void permute(int n, int *data) {
extern __shared__ int smem[];
if (n <= 1)
return;
smem[threadIdx.x] = data[threadIdx.x];
__syncthreads();
permute_data(smem, n);
__syncthreads();
// Write back to GMEM since we can't pass SMEM to children.
data[threadIdx.x] = smem[threadIdx.x];
__syncthreads();
if (threadIdx.x == 0) {
permute<<< 1, 256, n/2*sizeof(int) >>>(n/2, data);
permute<<< 1, 256, n/2*sizeof(int) >>>(n/2, data+n/2);
}
}
void host_launch(int *data) {
permute<<< 1, 256, 256*sizeof(int) >>>(256, data);
}
D.3.1.6.4.Symbol Addresses
Device-side symbols (i.e., those marked __device__) may be referenced from within a
kernel simply via the & operator, as all global-scope device variables are in the kernel's
visible address space. This also applies to __constant__ symbols, although in this case
the pointer will reference read-only data.
Given that device-side symbols can be referenced directly, those CUDA
runtime APIs which reference symbols (e.g., cudaMemcpyToSymbol() or
cudaGetSymbolAddress()) are redundant and hence not supported by the device
runtime. Note this implies that constant data cannot be altered from within a running
kernel, even ahead of a child kernel launch, as references to __constant__ space are
read-only.
D.3.1.7.API Errors and Launch Failures
As usual for the CUDA runtime, any function may return an error code. The last error
code returned is recorded and may be retrieved via the cudaGetLastError() call.
Errors are recorded per-thread, so that each thread can identify the most recent error
that it has generated. The error code is of type cudaError_t.
Similar to a host-side launch, device-side launches may fail for many reasons (invalid
arguments, etc). The user must call cudaGetLastError() to determine if a launch
generated an error, however lack of an error after launch does not imply the child kernel
completed successfully.
For device-side exceptions, e.g., access to an invalid address, an error in a child
grid will be returned to the host instead of being returned by the parent's call to
cudaDeviceSynchronize().
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D.3.1.7.1.Launch Setup APIs
Kernel launch is a system-level mechanism exposed through the device
runtime library, and as such is available directly from PTX via the underlying
cudaGetParameterBuffer() and cudaLaunchDevice() APIs. It is permitted for
a CUDA application to call these APIs itself, with the same requirements as for PTX.
In both cases, the user is then responsible for correctly populating all necessary data
structures in the correct format according to specification. Backwards compatibility is
guaranteed in these data structures.
As with host-side launch, the device-side operator <<<>>> maps to underlying kernel
launch APIs. This is so that users targeting PTX will be able to enact a launch, and so
that the compiler front-end can translate <<<>>> into these calls.
Table4 New Device-only Launch Implementation Functions
Runtime API Launch Functions
Description of Difference From Host
Runtime Behaviour (behaviour is identical if
no description)
cudaGetParameterBuffer Generated automatically from <<<>>>. Note
different API to host equivalent.
cudaLaunchDevice Generated automatically from <<<>>>. Note
different API to host equivalent.
The APIs for these launch functions are different to those of the CUDA Runtime API,
and are defined as follows:
extern device cudaError_t cudaGetParameterBuffer(void **params);
extern __device__ cudaError_t cudaLaunchDevice(void *kernel,
void *params, dim3 gridDim,
dim3 blockDim,
unsigned int sharedMemSize = 0,
cudaStream_t stream = 0);
D.3.1.8.API Reference
The portions of the CUDA Runtime API supported in the device runtime are detailed
here. Host and device runtime APIs have identical syntax; semantics are the same except
where indicated. The table below provides an overview of the API relative to the version
available from the host.
Table5 Supported API Functions
Runtime API Functions Details
cudaDeviceSynchronize Synchronizes on work launched from thread's own
block only
cudaDeviceGetCacheConfig
cudaDeviceGetLimit
cudaGetLastError Last error is per-thread state, not per-block state
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Runtime API Functions Details
cudaPeekAtLastError
cudaGetErrorString
cudaGetDeviceCount
cudaDeviceGetAttribute Will return attributes for any device
cudaGetDevice Always returns current device ID as would be seen
from host
cudaStreamCreateWithFlags Must pass cudaStreamNonBlocking flag
cudaStreamDestroy
cudaStreamWaitEvent
cudaEventCreateWithFlags Must pass cudaEventDisableTiming flag
cudaEventRecord
cudaEventDestroy
cudaFuncGetAttributes
cudaMemcpyAsync
cudaMemcpy2DAsync
cudaMemcpy3DAsync
cudaMemsetAsync
Notes about all memcpy/memset functions:
‣Only async memcpy/set functions are
supported
‣Only device-to-device memcpy is permitted
‣May not pass in local or shared memory
pointers
cudaMemset2DAsync
cudaMemset3DAsync
cudaRuntimeGetVersion
cudaMalloc
cudaFree
May not call cudaFree on the device on a pointer
created on the host, and vice-versa
cudaOccupancyMaxActiveBlocksPerMultiprocessor
cudaOccupancyMaxPotentialBlockSize
cudaOccupancyMaxPotentialBlockSizeVariableSMem
D.3.2.Device-side Launch from PTX
This section is for the programming language and compiler implementers who target
Parallel Thread Execution (PTX) and plan to support Dynamic Parallelism in their language.
It provides the low-level details related to supporting kernel launches at the PTX level.
D.3.2.1.Kernel Launch APIs
Device-side kernel launches can be implemented using the following two APIs
accessible from PTX: cudaLaunchDevice() and cudaGetParameterBuffer().
cudaLaunchDevice() launches the specified kernel with the parameter buffer that
is obtained by calling cudaGetParameterBuffer() and filled with the parameters
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to the launched kernel. The parameter buffer can be NULL, i.e., no need to invoke
cudaGetParameterBuffer(), if the launched kernel does not take any parameters.
D.3.2.1.1.cudaLaunchDevice
At the PTX level, cudaLaunchDevice()needs to be declared in one of the two forms
shown below before it is used.
// PTX-level Declaration of cudaLaunchDevice() when .address_size is 64
.extern .func(.param .b32 func_retval0) cudaLaunchDevice
(
.param .b64 func,
.param .b64 parameterBuffer,
.param .align 4 .b8 gridDimension[12],
.param .align 4 .b8 blockDimension[12],
.param .b32 sharedMemSize,
.param .b64 stream
)
;
// PTX-level Declaration of cudaLaunchDevice() when .address_size is 32
.extern .func(.param .b32 func_retval0) cudaLaunchDevice
(
.param .b32 func,
.param .b32 parameterBuffer,
.param .align 4 .b8 gridDimension[12],
.param .align 4 .b8 blockDimension[12],
.param .b32 sharedMemSize,
.param .b32 stream
)
;
The CUDA-level declaration below is mapped to one of the aforementioned PTX-level
declarations and is found in the system header file cuda_device_runtime_api.h.
The function is defined in the cudadevrt system library, which must be linked with a
program in order to use device-side kernel launch functionality.
// CUDA-level declaration of cudaLaunchDevice()
extern "C" __device__
cudaError_t cudaLaunchDevice(void *func, void *parameterBuffer,
dim3 gridDimension, dim3 blockDimension,
unsigned int sharedMemSize,
cudaStream_t stream);
The first parameter is a pointer to the kernel to be is launched, and the second parameter
is the parameter buffer that holds the actual parameters to the launched kernel. The
layout of the parameter buffer is explained in Parameter Buffer Layout, below. Other
parameters specify the launch configuration, i.e., as grid dimension, block dimension,
shared memory size, and the stream associated with the launch (please refer to
Execution Configuration for the detailed description of launch configuration.
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D.3.2.1.2.cudaGetParameterBuffer
cudaGetParameterBuffer() needs to be declared at the PTX level before it's used.
The PTX-level declaration must be in one of the two forms given below, depending on
address size:
// PTX-level Declaration of cudaGetParameterBuffer() when .address_size is 64
// When .address_size is 64
.extern .func(.param .b64 func_retval0) cudaGetParameterBuffer
(
.param .b64 alignment,
.param .b64 size
)
;
// PTX-level Declaration of cudaGetParameterBuffer() when .address_size is 32
.extern .func(.param .b32 func_retval0) cudaGetParameterBuffer
(
.param .b32 alignment,
.param .b32 size
)
;
The following CUDA-level declaration of cudaGetParameterBuffer() is mapped to
the aforementioned PTX-level declaration:
// CUDA-level Declaration of cudaGetParameterBuffer()
extern "C" __device__
void *cudaGetParameterBuffer(size_t alignment, size_t size);
The first parameter specifies the alignment requirement of the parameter buffer and
the second parameter the size requirement in bytes. In the current implementation, the
parameter buffer returned by cudaGetParameterBuffer() is always guaranteed to
be 64- byte aligned, and the alignment requirement parameter is ignored. However,
it is recommended to pass the correct alignment requirement value - which is
the largest alignment of any parameter to be placed in the parameter buffer - to
cudaGetParameterBuffer() to ensure portability in the future.
D.3.2.2.Parameter Buffer Layout
Parameter reordering in the parameter buffer is prohibited, and each individual
parameter placed in the parameter buffer is required to be aligned. That is, each
parameter must be placed at the nth byte in the parameter buffer, where n is the smallest
multiple of the parameter size that is greater than the offset of the last byte taken by the
preceding parameter. The maximum size of the parameter buffer is 4KB.
For a more detailed description of PTX code generated by the CUDA compiler, please
refer to the PTX-3.5 specification.
D.3.3.Toolkit Support for Dynamic Parallelism
D.3.3.1.Including Device Runtime API in CUDA Code
Similar to the host-side runtime API, prototypes for the CUDA device runtime API
are included automatically during program compilation. There is no need to include
cuda_device_runtime_api.h explicitly.
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D.3.3.2.Compiling and Linking
CUDA programs are automatically linked with the host runtime library when compiled
with nvcc, but the device runtime is shipped as a static library which must explicitly be
linked with a program which wishes to use it.
The device runtime is offered as a static library (cudadevrt.lib on Windows,
libcudadevrt.a under Linux and MacOS), against which a GPU application that uses
the device runtime must be linked. Linking of device libraries can be accomplished
through nvcc and/or nvlink. Two simple examples are shown below.
A device runtime program may be compiled and linked in a single step, if all required
source files can be specified from the command line:
$ nvcc -arch=sm_35 -rdc=true hello_world.cu -o hello -lcudadevrt
It is also possible to compile CUDA .cu source files first to object files, and then link
these together in a two-stage process:
$ nvcc -arch=sm_35 -dc hello_world.cu -o hello_world.o
$ nvcc -arch=sm_35 -rdc=true hello_world.o -o hello -lcudadevrt
Please see the Using Separate Compilation section of The CUDA Driver Compiler NVCC
guide for more details.
D.4.Programming Guidelines
D.4.1.Basics
The device runtime is a functional subset of the host runtime. API level device
management, kernel launching, device memcpy, stream management, and event
management are exposed from the device runtime.
Programming for the device runtime should be familiar to someone who already has
experience with CUDA. Device runtime syntax and semantics are largely the same as
that of the host API, with any exceptions detailed earlier in this document.
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The following example shows a simple Hello World program incorporating dynamic
parallelism:
#include <stdio.h>
__global__ void childKernel()
{
printf("Hello ");
}
__global__ void parentKernel()
{
// launch child
childKernel<<<1,1>>>();
if (cudaSuccess != cudaGetLastError()) {
return;
}
// wait for child to complete
if (cudaSuccess != cudaDeviceSynchronize()) {
return;
}
printf("World!\n");
}
int main(int argc, char *argv[])
{
// launch parent
parentKernel<<<1,1>>>();
if (cudaSuccess != cudaGetLastError()) {
return 1;
}
// wait for parent to complete
if (cudaSuccess != cudaDeviceSynchronize()) {
return 2;
}
return 0;
}
This program may be built in a single step from the command line as follows:
$ nvcc -arch=sm_35 -rdc=true hello_world.cu -o hello -lcudadevrt
D.4.2.Performance
D.4.2.1.Synchronization
Synchronization by one thread may impact the performance of other threads in the same
Thread Block, even when those other threads do not call cudaDeviceSynchronize()
themselves. This impact will depend upon the underlying implementation.
D.4.2.2.Dynamic-parallelism-enabled Kernel Overhead
System software which is active when controlling dynamic launches may impose an
overhead on any kernel which is running at the time, whether or not it invokes kernel
launches of its own. This overhead arises from the device runtime's execution tracking
and management software and may result in decreased performance for e.g., library
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calls when made from the device compared to from the host side. This overhead is, in
general, incurred for applications that link against the device runtime library.
D.4.3.Implementation Restrictions and Limitations
Dynamic Parallelism guarantees all semantics described in this document, however,
certain hardware and software resources are implementation-dependent and limit the
scale, performance and other properties of a program which uses the device runtime.
D.4.3.1.Runtime
D.4.3.1.1.Memory Footprint
The device runtime system software reserves memory for various management
purposes, in particular one reservation which is used for saving parent-grid state
during synchronization, and a second reservation for tracking pending grid launches.
Configuration controls are available to reduce the size of these reservations in exchange
for certain launch limitations. See Configuration Options, below, for details.
The majority of reserved memory is allocated as backing-store for parent kernel state, for
use when synchronizing on a child launch. Conservatively, this memory must support
storing of state for the maximum number of live threads possible on the device. This
means that each parent generation at which cudaDeviceSynchronize() is callable
may require up to 150MB of device memory, depending on the device configuration,
which will be unavailable for program use even if it is not all consumed.
D.4.3.1.2.Nesting and Synchronization Depth
Using the device runtime, one kernel may launch another kernel, and that kernel may
launch another, and so on. Each subordinate launch is considered a new nesting level,
and the total number of levels is the nesting depth of the program. The synchronization
depth is defined as the deepest level at which the program will explicitly synchronize
on a child launch. Typically this is one less than the nesting depth of the program, but
if the program does not need to call cudaDeviceSynchronize() at all levels then the
synchronization depth might be substantially different to the nesting depth.
The overall maximum nesting depth is limited to 24, but practically speaking the real
limit will be the amount of memory required by the system for each new level (see
Memory Footprint above). Any launch which would result in a kernel at a deeper level
than the maximum will fail. Note that this may also apply to cudaMemcpyAsync(),
which might itself generate a kernel launch. See Configuration Options for details.
By default, sufficient storage is reserved for two levels of synchronization. This
maximum synchronization depth (and hence reserved storage) may be controlled by
calling cudaDeviceSetLimit() and specifying cudaLimitDevRuntimeSyncDepth.
The number of levels to be supported must be configured before the top-level kernel is
launched from the host, in order to guarantee successful execution of a nested program.
Calling cudaDeviceSynchronize() at a depth greater than the specified maximum
synchronization depth will return an error.
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An optimization is permitted where the system detects that it need not
reserve space for the parent's state in cases where the parent kernel never calls
cudaDeviceSynchronize(). In this case, because explicit parent/child synchronization
never occurs, the memory footprint required for a program will be much less than
the conservative maximum. Such a program could specify a shallower maximum
synchronization depth to avoid over-allocation of backing store.
D.4.3.1.3.Pending Kernel Launches
When a kernel is launched, all associated configuration and parameter data is tracked
until the kernel completes. This data is stored within a system-managed launch pool.
The launch pool is divided into a fixed-size pool and a virtualized pool with lower
performance. The device runtime system software will try to track launch data in the
fixed-size pool first. The virtualized pool will be used to track new launches when the
fixed-size pool is full.
The size of the fixed-size launch pool is configurable by
calling cudaDeviceSetLimit() from the host and specifying
cudaLimitDevRuntimePendingLaunchCount.
D.4.3.1.4.Configuration Options
Resource allocation for the device runtime system software is controlled via the
cudaDeviceSetLimit() API from the host program. Limits must be set before
any kernel is launched, and may not be changed while the GPU is actively running
programs.
The following named limits may be set:
Limit Behavior
cudaLimitDevRuntimeSyncDepth Sets the maximum depth at which
cudaDeviceSynchronize() may be
called. Launches may be performed deeper
than this, but explicit synchronization
deeper than this limit will return the
cudaErrorLaunchMaxDepthExceeded. The
default maximum sync depth is 2.
cudaLimitDevRuntimePendingLaunchCount Controls the amount of memory set aside for
buffering kernel launches which have not yet
begun to execute, due either to unresolved
dependencies or lack of execution resources.
When the buffer is full, the device runtime
system software will attempt to track new
pending launches in a lower performance
virtualized buffer. If the virtualized buffer
is also full, i.e. when all available heap
space is consumed, launches will not occur,
and the thread's last error will be set to
cudaErrorLaunchPendingCountExceeded.
The default pending launch count is 2048
launches.
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D.4.3.1.5.Memory Allocation and Lifetime
cudaMalloc() and cudaFree() have distinct semantics between the host and device
environments. When invoked from the host, cudaMalloc() allocates a new region from
unused device memory. When invoked from the device runtime these functions map
to device-side malloc() and free(). This implies that within the device environment
the total allocatable memory is limited to the device malloc() heap size, which may
be smaller than the available unused device memory. Also, it is an error to invoke
cudaFree() from the host program on a pointer which was allocated by cudaMalloc()
on the device or vice-versa.
cudaMalloc() on Host cudaMalloc() on Device
cudaFree() on Host Supported Not Supported
cudaFree() on Device Not Supported Supported
Allocation limit Free device memory cudaLimitMallocHeapSize
D.4.3.1.6.SM Id and Warp Id
Note that in PTX %smid and %warpid are defined as volatile values. The device runtime
may reschedule thread blocks onto different SMs in order to more efficiently manage
resources. As such, it is unsafe to rely upon %smid or %warpid remaining unchanged
across the lifetime of a thread or thread block.
D.4.3.1.7.ECC Errors
No notification of ECC errors is available to code within a CUDA kernel. ECC errors
are reported at the host side once the entire launch tree has completed. Any ECC errors
which arise during execution of a nested program will either generate an exception or
continue execution (depending upon error and configuration).
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AppendixE.
MATHEMATICAL FUNCTIONS
The reference manual lists, along with their description, all the functions of the C/C++
standard library mathematical functions that are supported in device code, as well as all
intrinsic functions (that are only supported in device code).
This appendix provides accuracy information for some of these functions when
applicable.
E.1.Standard Functions
The functions from this section can be used in both host and device code.
This section specifies the error bounds of each function when executed on the device and
also when executed on the host in the case where the host does not supply the function.
The error bounds are generated from extensive but not exhaustive tests, so they are not
guaranteed bounds.
Single-Precision Floating-Point Functions
Addition and multiplication are IEEE-compliant, so have a maximum error of 0.5 ulp.
The recommended way to round a single-precision floating-point operand to an
integer, with the result being a single-precision floating-point number is rintf(),
not roundf(). The reason is that roundf() maps to an 8-instruction sequence on
the device, whereas rintf() maps to a single instruction. truncf(), ceilf(), and
floorf() each map to a single instruction as well.
Table6 Single-Precision Mathematical Standard Library Functions with
Maximum ULP Error
The maximum error is stated as the absolute value of the difference in ulps between a
correctly rounded single-precision result and the result returned by the CUDA library
function.
Mathematical Functions
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Function Maximum ulp error
x+y 0 (IEEE-754 round-to-nearest-even)
x*y 0 (IEEE-754 round-to-nearest-even)
x/y 0 for compute capability ≥ 2 when compiled with -prec-
div=true
2 (full range), otherwise
1/x 0 for compute capability ≥ 2 when compiled with -prec-
div=true
1 (full range), otherwise
rsqrtf(x)
1/sqrtf(x)
2 (full range)
Applies to 1/sqrtf(x) only when it is converted to
rsqrtf(x) by the compiler.
sqrtf(x) 0 for compute capability ≥ 2 when compiled with -prec-
sqrt=true
3 (full range), otherwise
cbrtf(x) 1 (full range)
rcbrtf(x) 1 (full range)
hypotf(x,y) 3 (full range)
rhypotf(x,y) 2 (full range)
norm3df(x,y,z) 3 (full range)
rnorm3df(x,y,z) 2 (full range)
norm4df(x,y,z,t) 3 (full range)
rnorm4df(x,y,z,t) 2 (full range)
normf(dim,arr) 4 (full range)
rnormf(dim,arr) 3 (full range)
expf(x) 2 (full range)
exp2f(x) 2 (full range)
exp10f(x) 2 (full range)
expm1f(x) 1 (full range)
logf(x) 1 (full range)
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Function Maximum ulp error
log2f(x) 1 (full range)
log10f(x) 2 (full range)
log1pf(x) 1 (full range)
sinf(x) 2 (full range)
cosf(x) 2 (full range)
tanf(x) 4 (full range)
sincosf(x,sptr,cptr) 2 (full range)
sinpif(x) 2 (full range)
cospif(x) 2 (full range)
sincospif(x,sptr,cptr) 2 (full range)
asinf(x) 4 (full range)
acosf(x) 3 (full range)
atanf(x) 2 (full range)
atan2f(y,x) 3 (full range)
sinhf(x) 3 (full range)
coshf(x) 2 (full range)
tanhf(x) 2 (full range)
asinhf(x) 3 (full range)
acoshf(x) 4 (full range)
atanhf(x) 3 (full range)
powf(x,y) 8 (full range)
erff(x) 2 (full range)
erfcf(x) 4 (full range)
erfinvf(x) 2 (full range)
erfcinvf(x) 2 (full range)
erfcxf(x) 4 (full range)
normcdff(x) 5 (full range)
normcdfinvf(x) 5 (full range)
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Function Maximum ulp error
lgammaf(x) 6 (outside interval -10.001 ... -2.264; larger inside)
tgammaf(x) 11 (full range)
fmaf(x,y,z) 0 (full range)
frexpf(x,exp) 0 (full range)
ldexpf(x,exp) 0 (full range)
scalbnf(x,n) 0 (full range)
scalblnf(x,l) 0 (full range)
logbf(x) 0 (full range)
ilogbf(x) 0 (full range)
j0f(x) 9 for |x| < 8
otherwise, the maximum absolute error is 2.2 x 10-6
j1f(x) 9 for |x| < 8
otherwise, the maximum absolute error is 2.2 x 10-6
jnf(x) For n = 128, the maximum absolute error is 2.2 x 10-6
y0f(x) 9 for |x| < 8
otherwise, the maximum absolute error is 2.2 x 10-6
y1f(x) 9 for |x| < 8
otherwise, the maximum absolute error is 2.2 x 10-6
ynf(x) ceil(2 + 2.5n) for |x| < n
otherwise, the maximum absolute error is 2.2 x 10-6
cyl_bessel_i0f(x) 6 (full range)
cyl_bessel_i1f(x) 6 (full range)
fmodf(x,y) 0 (full range)
remainderf(x,y) 0 (full range)
remquof(x,y,iptr) 0 (full range)
modff(x,iptr) 0 (full range)
fdimf(x,y) 0 (full range)
truncf(x) 0 (full range)
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Function Maximum ulp error
roundf(x) 0 (full range)
rintf(x) 0 (full range)
nearbyintf(x) 0 (full range)
ceilf(x) 0 (full range)
floorf(x) 0 (full range)
lrintf(x) 0 (full range)
lroundf(x) 0 (full range)
llrintf(x) 0 (full range)
llroundf(x) 0 (full range)
Double-Precision Floating-Point Functions
The recommended way to round a double-precision floating-point operand to an
integer, with the result being a double-precision floating-point number is rint(), not
round(). The reason is that round() maps to an 8-instruction sequence on the device,
whereas rint() maps to a single instruction. trunc(), ceil(), and floor() each map
to a single instruction as well.
Table7 Double-Precision Mathematical Standard Library Functions with
Maximum ULP Error
The maximum error is stated as the absolute value of the difference in ulps between a
correctly rounded double-precision result and the result returned by the CUDA library
function.
Function Maximum ulp error
x+y 0 (IEEE-754 round-to-nearest-even)
x*y 0 (IEEE-754 round-to-nearest-even)
x/y 0 (IEEE-754 round-to-nearest-even)
1/x 0 (IEEE-754 round-to-nearest-even)
sqrt(x) 0 (IEEE-754 round-to-nearest-even)
rsqrt(x) 1 (full range)
cbrt(x) 1 (full range)
rcbrt(x) 1 (full range)
Mathematical Functions
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Function Maximum ulp error
hypot(x,y) 2 (full range)
rhypot(x,y) 1 (full range)
norm3d(x,y,z) 2 (full range)
rnorm3d(x,y,z) 1 (full range)
norm4d(x,y,z,t) 2 (full range)
rnorm4d(x,y,z,t) 1 (full range)
norm(dim,arr) 3 (full range)
rnorm(dim,arr) 2 (full range)
exp(x) 1 (full range)
exp2(x) 1 (full range)
exp10(x) 1 (full range)
expm1(x) 1 (full range)
log(x) 1 (full range)
log2(x) 1 (full range)
log10(x) 1 (full range)
log1p(x) 1 (full range)
sin(x) 1 (full range)
cos(x) 1 (full range)
tan(x) 2 (full range)
sincos(x,sptr,cptr) 1 (full range)
sinpi(x) 1 (full range)
cospi(x) 1 (full range)
sincospi(x,sptr,cptr) 1 (full range)
asin(x) 2 (full range)
acos(x) 1 (full range)
atan(x) 2 (full range)
atan2(y,x) 2 (full range)
sinh(x) 1 (full range)
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Function Maximum ulp error
cosh(x) 1 (full range)
tanh(x) 1 (full range)
asinh(x) 2 (full range)
acosh(x) 2 (full range)
atanh(x) 2 (full range)
pow(x,y) 2 (full range)
erf(x) 2 (full range)
erfc(x) 4 (full range)
erfinv(x) 5 (full range)
erfcinv(x) 6 (full range)
erfcx(x) 3 (full range)
normcdf(x) 5 (full range)
normcdfinv(x) 7 (full range)
lgamma(x) 4 (outside interval -11.0001 ... -2.2637; larger
inside)
tgamma(x) 8 (full range)
fma(x,y,z) 0 (IEEE-754 round-to-nearest-even)
frexp(x,exp) 0 (full range)
ldexp(x,exp) 0 (full range)
scalbn(x,n) 0 (full range)
scalbln(x,l) 0 (full range)
logb(x) 0 (full range)
ilogb(x) 0 (full range)
j0(x) 7 for |x| < 8
otherwise, the maximum absolute error is 5 x
10-12
j1(x) 7 for |x| < 8
otherwise, the maximum absolute error is 5 x
10-12
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Function Maximum ulp error
jn(x) For n = 128, the maximum absolute error is 5 x
10-12
y0(x) 7 for |x| < 8
otherwise, the maximum absolute error is 5 x
10-12
y1(x) 7 for |x| < 8
otherwise, the maximum absolute error is 5 x
10-12
yn(x) For |x| > 1.5n, the maximum absolute error is 5
x 10-12
cyl_bessel_i0(x) 6 (full range)
cyl_bessel_i1(x) 6 (full range)
fmod(x,y) 0 (full range)
remainder(x,y) 0 (full range)
remquo(x,y,iptr) 0 (full range)
mod(x,iptr) 0 (full range)
fdim(x,y) 0 (full range)
trunc(x) 0 (full range)
round(x) 0 (full range)
rint(x) 0 (full range)
nearbyint(x) 0 (full range)
ceil(x) 0 (full range)
floor(x) 0 (full range)
lrint(x) 0 (full range)
lround(x) 0 (full range)
llrint(x) 0 (full range)
llround(x) 0 (full range)
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E.2.Intrinsic Functions
The functions from this section can only be used in device code.
Among these functions are the less accurate, but faster versions of some of the functions
of Standard Functions .They have the same name prefixed with __ (such as __sinf(x)).
They are faster as they map to fewer native instructions. The compiler has an option
(-use_fast_math) that forces each function in Table 8 to compile to its intrinsic
counterpart. In addition to reducing the accuracy of the affected functions, it may
also cause some differences in special case handling. A more robust approach is to
selectively replace mathematical function calls by calls to intrinsic functions only where
it is merited by the performance gains and where changed properties such as reduced
accuracy and different special case handling can be tolerated.
Table8 Functions Affected by -use_fast_math
Operator/Function Device Function
x/y __fdividef(x,y)
sinf(x) __sinf(x)
cosf(x) __cosf(x)
tanf(x) __tanf(x)
sincosf(x,sptr,cptr) __sincosf(x,sptr,cptr)
logf(x) __logf(x)
log2f(x) __log2f(x)
log10f(x) __log10f(x)
expf(x) __expf(x)
exp10f(x) __exp10f(x)
powf(x,y) __powf(x,y)
Functions suffixed with _rn operate using the round to nearest even rounding mode.
Functions suffixed with _rz operate using the round towards zero rounding mode.
Functions suffixed with _ru operate using the round up (to positive infinity) rounding
mode.
Functions suffixed with _rd operate using the round down (to negative infinity)
rounding mode.
Single-Precision Floating-Point Functions
__fadd_[rn,rz,ru,rd]() and __fmul_[rn,rz,ru,rd]() map to addition and
multiplication operations that the compiler never merges into FMADs. By contrast,
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additions and multiplications generated from the '*' and '+' operators will frequently be
combined into FMADs.
The accuracy of floating-point division varies depending on whether the code is
compiled with -prec-div=false or -prec-div=true. When the code is compiled
with -prec-div=false, both the regular division / operator and __fdividef(x,y)
have the same accuracy, but for 2126 < y < 2128, __fdividef(x,y) delivers a result of
zero, whereas the / operator delivers the correct result to within the accuracy stated
in Table 9. Also, for 2126 < y < 2128, if x is infinity, __fdividef(x,y) delivers a NaN (as
a result of multiplying infinity by zero), while the / operator returns infinity. On the
other hand, the / operator is IEEE-compliant when the code is compiled with -prec-
div=true or without any -prec-div option at all since its default value is true.
Table9 Single-Precision Floating-Point Intrinsic Functions
(Supported by the CUDA Runtime Library with Respective Error Bounds)
Function Error bounds
__fadd_[rn,rz,ru,rd](x,y) IEEE-compliant.
__fsub_[rn,rz,ru,rd](x,y) IEEE-compliant.
__fmul_[rn,rz,ru,rd](x,y) IEEE-compliant.
__fmaf_[rn,rz,ru,rd](x,y,z) IEEE-compliant.
__frcp_[rn,rz,ru,rd](x) IEEE-compliant.
__fsqrt_[rn,rz,ru,rd](x) IEEE-compliant.
__frsqrt_rn(x) IEEE-compliant.
__fdiv_[rn,rz,ru,rd](x,y) IEEE-compliant.
__fdividef(x,y) For y in [2-126, 2126], the maximum ulp error is 2.
__expf(x) The maximum ulp error is 2 + floor(abs(1.16
* x)).
__exp10f(x) The maximum ulp error is 2+ floor(abs(2.95 *
x)).
__logf(x) For x in [0.5, 2], the maximum absolute error is
2-21.41, otherwise, the maximum ulp error is 3.
__log2f(x) For x in [0.5, 2], the maximum absolute error is
2-22, otherwise, the maximum ulp error is 2.
__log10f(x) For x in [0.5, 2], the maximum absolute error is
2-24, otherwise, the maximum ulp error is 3.
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Function Error bounds
__sinf(x) For x in [-π,π], the maximum absolute error is
2-21.41, and larger otherwise.
__cosf(x) For x in [-π,π], the maximum absolute error is
2-21.19, and larger otherwise.
__sincosf(x,sptr,cptr) Same as __sinf(x) and __cosf(x).
__tanf(x) Derived from its implementation as __sinf(x) *
(1/__cosf(x)).
__powf(x, y) Derived from its implementation as exp2f(y *
__log2f(x)).
Double-Precision Floating-Point Functions
__dadd_rn() and __dmul_rn() map to addition and multiplication operations that
the compiler never merges into FMADs. By contrast, additions and multiplications
generated from the '*' and '+' operators will frequently be combined into FMADs.
Table10 Double-Precision Floating-Point Intrinsic Functions
(Supported by the CUDA Runtime Library with Respective Error Bounds)
Function Error bounds
__dadd_[rn,rz,ru,rd](x,y) IEEE-compliant.
__dsub_[rn,rz,ru,rd](x,y) IEEE-compliant.
__dmul_[rn,rz,ru,rd](x,y) IEEE-compliant.
__fma_[rn,rz,ru,rd](x,y,z) IEEE-compliant.
__ddiv_[rn,rz,ru,rd](x,y)(x,y) IEEE-compliant.
Requires compute capability > 2.
__drcp_[rn,rz,ru,rd](x) IEEE-compliant.
Requires compute capability > 2.
__dsqrt_[rn,rz,ru,rd](x) IEEE-compliant.
Requires compute capability > 2.
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AppendixF.
C/C++ LANGUAGE SUPPORT
As described in Compilation with NVCC, CUDA source files compiled with nvcc can
include a mix of host code and device code. The CUDA frontend compiler aims to
emulate the host compiler behavior with respect to C++ input code. The input source
code is processed according to the C++ ISO/IEC 14882:2003, C++ ISO/IEC 14882:2011 or C
++ ISO/IEC 14882:2014 specifications, and the CUDA frontend compiler aims to emulate
any host compiler divergences from the ISO specification. In addition, the supported
language is extended with CUDA-specific constructs described in this document 7, and
is subject to the restrictions described below.
C++11 Language Features and C++14 Language Features provide support matrices for
the C++11 and C++14 features, respectively. Restrictions lists the language restrictions.
Polymorphic Function Wrappers and Experimental Feature: Extended Lambdas describe
additional features. Code Samples gives code samples.
F.1.C++11 Language Features
The following table lists new language features that have been accepted into the C++11
standard. The "Proposal" column provides a link to the ISO C++ committee proposal
that describes the feature, while the "Available in nvcc (device code)" column indicates
the first version of nvcc that contains an implementation of this feature (if it has been
implemented) for device code.
Table11 C++11 Language Features
Language Feature C++11
Proposal
Available
in nvcc
(device
code)
Rvalue references N2118 7.0
Rvalue references for *this N2439 7.0
Initialization of class objects by rvalues N1610 7.0
7e.g., the <<<...>>> syntax for launching kernels.
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Language Feature C++11
Proposal
Available
in nvcc
(device
code)
Non-static data member initializers N2756 7.0
Variadic templates N2242 7.0
Extending variadic template template parameters N2555 7.0
Initializer lists N2672 7.0
Static assertions N1720 7.0
auto-typed variables N1984 7.0
Multi-declarator auto N1737 7.0
Removal of auto as a storage-class specifier N2546 7.0
New function declarator syntax N2541 7.0
Lambda expressions N2927 7.0
Declared type of an expression N2343 7.0
Incomplete return types N3276 7.0
Right angle brackets N1757 7.0
Default template arguments for function templates DR226 7.0
Solving the SFINAE problem for expressions DR339 7.0
Alias templates N2258 7.0
Extern templates N1987 7.0
Null pointer constant N2431 7.0
Strongly-typed enums N2347 7.0
Forward declarations for enums
N2764
DR1206
7.0
Standardized attribute syntax N2761 7.0
Generalized constant expressions N2235 7.0
Alignment support N2341 7.0
Conditionally-support behavior N1627 7.0
Changing undefined behavior into diagnosable errors N1727 7.0
Delegating constructors N1986 7.0
Inheriting constructors N2540 7.0
Explicit conversion operators N2437 7.0
New character types N2249 7.0
Unicode string literals N2442 7.0
Raw string literals N2442 7.0
Universal character names in literals N2170 7.0
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Language Feature C++11
Proposal
Available
in nvcc
(device
code)
User-defined literals N2765 7.0
Standard Layout Types N2342 7.0
Defaulted functions N2346 7.0
Deleted functions N2346 7.0
Extended friend declarations N1791 7.0
Extending sizeof
N2253
DR850
7.0
Inline namespaces N2535 7.0
Unrestricted unions N2544 7.0
Local and unnamed types as template arguments N2657 7.0
Range-based for N2930 7.0
Explicit virtual overrides
N2928
N3206
N3272
7.0
Minimal support for garbage collection and reachability-based leak
detection N2670 N/A (see
Restrictions)
Allowing move constructors to throw [noexcept] N3050 7.0
Defining move special member functions N3053 7.0
Concurrency
Sequence points N2239
Atomic operations N2427
Strong Compare and Exchange N2748
Bidirectional Fences N2752
Memory model N2429
Data-dependency ordering: atomics and memory model N2664
Propagating exceptions N2179
Allow atomics use in signal handlers N2547
Thread-local storage N2659
Dynamic initialization and destruction with concurrency N2660
C99 Features in C++11
__func__ predefined identifier N2340 7.0
C99 preprocessor N1653 7.0
long long N1811 7.0
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Language Feature C++11
Proposal
Available
in nvcc
(device
code)
Extended integral types N1988
F.2.C++14 Language Features
The following table lists new language features that have been accepted into the C++14
standard.
Table12 C++14 Language Features
Language Feature C++14
Proposal
Available
in nvcc
(device
code)
Tweak to certain C++ contextual conversions N3323 9.0
Binary literals N3472 9.0
Functions with deduced return type N3638 9.0
Generalized lambda capture (init-capture) N3648 9.0
Generic (polymorphic) lambda expressions N3649 9.0
Variable templates N3651 9.0
Relaxing requirements on constexpr functions N3652 9.0
Member initializers and aggregates N3653 9.0
Clarifying memory allocation N3664
Sized deallocation N3778
[[deprecated]] attribute N3760 9.0
Single-quotation-mark as a digit separator N3781 9.0
F.3.Restrictions
F.3.1.Host Compiler Extensions
Host compiler specific language extensions are not supported in device code. __float128
and __float80 builtin types are not supported in both host and device code.
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F.3.2.Preprocessor Symbols
F.3.2.1.__CUDA_ARCH__
1. The type signature of the following entities shall not depend on whether
__CUDA_ARCH__ is defined or not, or on a particular value of __CUDA_ARCH__:
‣__global__ functions and function templates
‣__device__ and __constant__ variables
‣textures and surfaces
Example:
#if !defined(__CUDA_ARCH__)
typedef int mytype;
#else
typedef double mytype;
#endif
__device__ mytype xxx; // error: xxx's type depends on __CUDA_ARCH__
__global__ void foo(mytype in, // error: foo's type depends on __CUDA_ARCH__
mytype *ptr)
{
*ptr = in;
}
2. If a __global__ function template is instantiated and launched from the host,
then the function template must be instantiated with the same template arguments
irrespective of whether __CUDA_ARCH__ is defined and regardless of the value of
__CUDA_ARCH__.
Example:
__device__ int result;
template <typename T>
__global__ void kern(T in)
{
result = in;
}
__host__ __device__ void foo(void)
{
#if !defined(__CUDA_ARCH__)
kern<<<1,1>>>(1); // error: "kern<int>" instantiation only
// when __CUDA_ARCH__ is undefined!
#endif
}
int main(void)
{
foo();
cudaDeviceSynchronize();
return 0;
}
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3. In separate compilation mode, the presence or absence of a definition of a function
or variable with external linkage shall not depend on whether __CUDA_ARCH__ is
defined or on a particular value of __CUDA_ARCH__ 8.
Example:
#if !defined(__CUDA_ARCH__)
void foo(void) { } // error: The definition of foo()
// is only present when __CUDA_ARCH__
// is undefined
#endif
4. In separate compilation, __CUDA_ARCH__ must not be used in headers such that
different objects could contain different behavior. Or, it must be guaranteed that
all objects will compile for the same compute_arch. If a weak function or template
function is defined in a header and its behavior depends on __CUDA_ARCH__, then
the instances of that function in the objects could conflict if the objects are compiled
for different compute arch.
For example, if an a.h contains:
template<typename T>
__device__ T* getptr(void)
{
#if __CUDA_ARCH__ == 200
return NULL; /* no address */
#else
__shared__ T arr[256];
return arr;
#endif
}
Then if a.cu and b.cu both include a.h and instantiate getptr for the same type, and
b.cu expects a non-NULL address, and compile with:
nvcc –arch=compute_20 –dc a.cu
nvcc –arch=compute_30 –dc b.cu
nvcc –arch=sm_30 a.o b.o
At link time only one version of the getptr is used, so the behavior would depend
on which version is picked. To avoid this, either a.cu and b.cu must be compiled for
the same compute arch, or __CUDA_ARCH__ should not be used in the shared header
function.
The compiler does not guarantee that a diagnostic will be generated for the unsupported
uses of __CUDA_ARCH__ described above.
F.3.3.Qualifiers
F.3.3.1.Device Memory Space Specifiers
The __device__, __shared__ and __constant__ memory space specifiers are not
allowed on:
‣class, struct, and union data members,
8This does not apply to entities that may be defined in more than one translation unit, such as compiler generated
template instantiations.
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‣formal parameters,
‣local variables within a function that executes on the host.
__shared__ and __constant__ variables have implied static storage.
__device__ and __constant__ variable definitions are only allowed in namespace
scope (including global namespace scope).
__device__, __constant__ and __shared__ variables defined in namespace scope,
that are of class type, cannot have a non-empty constructor or a non-empty destructor. A
constructor for a class type is considered empty at a point in the translation unit, if it is
either a trivial constructor or it satisfies all of the following conditions:
‣The constructor function has been defined.
‣The constructor function has no parameters, the initializer list is empty and the
function body is an empty compound statement.
‣Its class has no virtual functions and no virtual base classes.
‣The default constructors of all base classes of its class can be considered empty.
‣For all the nonstatic data members of its class that are of class type (or array thereof),
the default constructors can be considered empty.
A destructor for a class is considered empty at a point in the translation unit, if it is
either a trivial destructor or it satisfies all of the following conditions:
‣The destructor function has been defined.
‣The destructor function body is an empty compound statement.
‣Its class has no virtual functions and no virtual base classes.
‣The destructors of all base classes of its class can be considered empty.
‣For all the nonstatic data members of its class that are of class type (or array thereof),
the destructor can be considered empty.
When compiling in the whole program compilation mode (see the nvcc user manual for
a description of this mode), __device__, __shared__, and __constant__ variables
cannot be defined as external using the extern keyword. The only exception is for
dynamically allocated __shared__ variables as described in __shared__.
When compiling in the separate compilation mode (see the nvcc user manual for a
description of this mode), __device__, __shared__, and __constant__ variables can
be defined as external using the extern keyword. nvlink will generate an error when
it cannot find a definition for an external variable (unless it is a dynamically allocated
__shared__ variable).
F.3.3.2.__managed__ Memory Space Specifier
Variables marked with the __managed__ memory space specifier ("managed" variables)
have the following restrictions:
‣The address of a managed variable is not a constant expression.
‣A managed variable shall not have a const qualified type.
‣A managed variable shall not have a reference type.
‣The address or value of a managed variable shall not be used when the CUDA
runtime may not be in a valid state, including the following cases:
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‣In static/dynamic initialization or destruction of an object with static or thread
local storage duration.
‣In code that executes after exit() has been called (e.g., a function marked with
gcc's "__attribute__((destructor))").
‣In code that executes when CUDA runtime may not be initialized (e.g., a
function marked with gcc's "__attribute__((constructor))").
‣A managed variable cannot be used as an unparenthesized id-expression argument
to a decltype() expression.
‣Managed variables have the same coherence and consistency behavior as specified
for dynamically allocated managed memory.
‣When a CUDA program containing managed variables is run on an execution
platform with multiple GPUs, the variables are allocated only once, and not per
GPU.
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Here are examples of legal and illegal uses of managed variables:
__device__ __managed__ int xxx = 10; // OK
int *ptr = &xxx; // error: use of managed variable
// (xxx) in static initialization
struct S1_t {
int field;
S1_t(void) : field(xxx) { };
};
struct S2_t {
~S2_t(void) { xxx = 10; }
};
S1_t temp1; // error: use of managed variable
// (xxx) in dynamic initialization
S2_t temp2; // error: use of managed variable
// (xxx) in the destructor of
// object with static storage
// duration
__device__ __managed__ const int yyy = 10; // error: const qualified type
__device__ __managed__ int &zzz = xxx; // error: reference type
template <int *addr> struct S3_t { };
S3_t<&xxx> temp; // error: address of managed
// variable(xxx) not a
// constant expression
__global__ void kern(int *ptr)
{
assert(ptr == &xxx); // OK
xxx = 20; // OK
}
int main(void)
{
int *ptr = &xxx; // OK
kern<<<1,1>>>(ptr);
cudaDeviceSynchronize();
xxx++; // OK
decltype(xxx) qqq; // error: managed variable(xxx) used
// as unparenthized argument to
// decltype
decltype((xxx)) zzz = yyy; // OK
}
F.3.3.3.Volatile Qualifier
The compiler is free to optimize reads and writes to global or shared memory (for
example, by caching global reads into registers or L1 cache) as long as it respects the
memory ordering semantics of memory fence functions (Memory Fence Functions) and
memory visibility semantics of synchronization functions (Synchronization Functions).
These optimizations can be disabled using the volatile keyword: If a variable located
in global or shared memory is declared as volatile, the compiler assumes that its value
can be changed or used at any time by another thread and therefore any reference to this
variable compiles to an actual memory read or write instruction.
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F.3.4.Pointers
Dereferencing a pointer either to global or shared memory in code that is executed
on the host, or to host memory in code that is executed on the device results in an
undefined behavior, most often in a segmentation fault and application termination.
The address obtained by taking the address of a __device__, __shared__ or
__constant__ variable can only be used in device code. The address of a __device__
or __constant__ variable obtained through cudaGetSymbolAddress() as described
in Device Memory can only be used in host code.
As a consequence of the use of C++ syntax rules, void pointers (e.g., returned by
malloc()) cannot be assigned to non-void pointers without a typecast.
F.3.5.Operators
F.3.5.1.Assignment Operator
__constant__ variables can only be assigned from the host code through runtime
functions (Device Memory); they cannot be assigned from the device code.
__shared__ variables cannot have an initialization as part of their declaration.
It is not allowed to assign values to any of the built-in variables defined in Built-in
Variables.
F.3.5.2.Address Operator
It is not allowed to take the address of any of the built-in variables defined in Built-in
Variables.
F.3.6.Run Time Type Information (RTTI)
The following RTTI-related features are supported in host code, but not in device code.
‣typeid operator
‣std::type_info
‣dynamic_cast operator
F.3.7.Exception Handling
Exception handling is only supported in host code, but not in device code.
F.3.8.Standard Library
Standard libraries are only supported in host code, but not in device code, unless
specified otherwise.
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F.3.9.Functions
F.3.9.1.External Linkage
A call within some device code of a function declared with the extern qualifier is only
allowed if the function is defined within the same compilation unit as the device code,
i.e., a single file or several files linked together with relocatable device code and nvlink.
F.3.9.2.Compiler generated functions
The execution space specifiers (__host__, __device__) for a compiler generated
function are the union of the execution space specifiers of all the functions that invoke it
(note that a __global__ caller will be treated as a __device__ caller for this analysis).
For example:
class Base {
int x;
public:
__host__ __device__ Base(void) : x(10) {}
};
class Derived : public Base {
int y;
};
class Other: public Base {
int z;
};
__device__ void foo(void)
{
Derived D1;
Other D2;
}
__host__ void bar(void)
{
Other D3;
}
Here, the compiler generated constructor function "Derived::Derived" will be treated
as a __device__ function, since it is invoked only from the __device__ function
"foo". The compiler generated constructor function "Other::Other" will be treated as a
__host__ __device__ function, since it is invoked both from a __device__ function
"foo" and a __host__ function "bar".
F.3.9.3.Function Parameters
__global__ function parameters are passed to the device via constant memory and are
limited to 4 KB.
__global__ functions cannot have a variable number of arguments.
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F.3.9.4.Static Variables within Function
Within the body of a __device__ or __global__ function, only __shared__ variables
or variables without any device memory space specifiers may be declared with
static storage class. Within the body of a __device__ __host__ function, only
unannotated static variables (i.e., without device memory space specifiers) may be
declared with static storage class. Unannotated function-scope static variables have the
same restrictions as __device__ variables defined in namespace scope. They cannot
have a non-empty constructor or a non-empty destructor, if they are of class type (see
Device Memory Space Specifiers).
Examples of legal and illegal uses of function-scope static variables are shown below.
struct S1_t {
int x;
};
struct S2_t {
int x;
__device__ S2_t(void) { x = 10; }
};
struct S3_t {
int x;
__device__ S3_t(int p) : x(p) { }
};
__device__ void f1() {
static int i1; // OK
static int i2 = 11; // OK
static S1_t i3; // OK
static S1_t i4 = {22}; // OK
static __shared__ int i5; // OK
int x = 33;
static int i6 = x; // error: dynamic initialization is not allowed
static S1_t i7 = {x}; // error: dynamic initialization is not allowed
static S2_t i8; // error: dynamic initialization is not allowed
static S3_t i9(44); // error: dynamic initialization is not allowed
}
__host__ __device__ void f2() {
static int i1; // OK
static __shared__ int i2; // error: __shared__ variable inside
// a host function
}
F.3.9.5.Function Pointers
The address of a __global__ function taken in host code cannot be used in device code
(e.g. to launch the kernel). Similarly, the address of a __global__ function taken in
device code 9 cannot be used in host code.
It is not allowed to take the address of a __device__ function in host code.
9supported with architectures >= sm_35
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F.3.9.6.Function Recursion
__global__ functions do not support recursion.
F.3.9.7.Friend Functions
A __global__ function or function template cannot be defined in a friend declaration.
Example:
struct S1_t {
friend __global__
void foo1(void); // OK: not a definition
template<typename T>
friend __global__
void foo2(void); // OK: not a definition
friend __global__
void foo3(void) { } // error: definition in friend declaration
template<typename T>
friend __global__
void foo4(void) { } // error: definition in friend declaration
};
F.3.9.8.Operator Function
An operator function cannot be a __global__ function.
F.3.10.Classes
F.3.10.1.Data Members
Static data members are not supported except for those that are also const-qualified (see
Const-qualified variables).
F.3.10.2.Function Members
Static member functions cannot be __global__ functions.
F.3.10.3.Virtual Functions
When a function in a derived class overrides a virtual function in a base class, the
execution space specifiers (i.e., __host__, __device__) on the overridden and
overriding functions must match.
It is not allowed to pass as an argument to a __global__ function an object of a class
with virtual functions.
The virtual function table is placed in global or constant memory by the compiler.
F.3.10.4.Virtual Base Classes
It is not allowed to pass as an argument to a __global__ function an object of a class
derived from virtual base classes.
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F.3.10.5.Anonymous Unions
Member variables of a namespace scope anonymous union cannot be referenced in a
__global__ or __device__ function.
F.3.10.6.Windows-Specific
The CUDA compiler follows the IA64 ABI for class layout, while the Microsoft host
compiler does not. This may cause the CUDA compiler to compute the class layout and
size differently than the Microsoft host compiler, for a class type 'T' that satisfies any of
the following conditions or for any class type that has T as a field type or as a base class
type (direct or indirect):
‣T has virtual functions.
‣T has a virtual base class.
‣T has multiple inheritance with more than one direct or indirect empty base class.
‣All direct and indirect base classes ('B') of T are empty and the type of the first field
of T ('F') uses B in its definition, such that B is laid out at offset 0 in the definition of
F.
As long as affected class types are used exclusively in host or device code, the program
should work correctly; do not pass objects of such class types between between host and
device code (e.g., as arguments to __global__ functions or through cudaMemcpy*()
calls) 10.
F.3.11.Templates
A type or template cannot be used in the type, non-type or template template argument
of a __global__ function template instantiation or a __device__/__constant__
variable instantiation if either:
‣The type or template is defined within a __host__ or __host__ __device__.
‣The type or template is a class member with private or protected access and its
parent class is not defined within a __device__ or __global__ function.
‣The type is unnamed.
‣The type is compounded from any of the types above.
10 One way to debug suspected layout mismatch of a type C is to use printf to output the values of sizeof(C) and
offsetof(C, field) in host and device code.
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Example:
template <typename T>
__global__ void myKernel(void) { }
class myClass {
private:
struct inner_t { };
public:
static void launch(void)
{
// error: inner_t is used in template argument
// but it is private
myKernel<inner_t><<<1,1>>>();
}
};
// C++14 only
template <typename T> __device__ T d1;
template <typename T1, typename T2> __device__ T1 d2;
void fn() {
struct S1_t { };
// error (C++14 only): S1_t is local to the function fn
d1<S1_t> = {};
auto lam1 = [] { };
// error (C++14 only): a closure type cannot be used for
// instantiating a variable template
d2<int, decltype(lam1)> = 10;
}
F.3.12.Trigraphs and Digraphs
Trigraphs are not supported on any platform. Digraphs are not supported on Windows.
F.3.13.Const-qualified variables
Let 'V' denote a namespace scope variable or a class static member variable that has
const qualified type and does not have execution space annotations (e.g., __device__,
__constant__, __shared__). V is considered to be a host code variable.
The value of V may be directly used in device code, if
‣V has been initialized with a constant expression before the point of use,
‣the type of V is not volatile-qualified, and
‣it has one of the following types:
‣builtin floating point type except when the Microsoft compiler is used as the
host compiler,
‣builtin integral type.
Device source code cannot contain a reference to V or take the address of V.
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Example:
const int xxx = 10;
struct S1_t { static const int yyy = 20; };
extern const int zzz;
const float www = 5.0;
__device__ void foo(void) {
int local1[xxx]; // OK
int local2[S1_t::yyy]; // OK
int val1 = xxx; // OK
int val2 = S1_t::yyy; // OK
int val3 = zzz; // error: zzz not initialized with constant
// expression at the point of use.
const int &val3 = xxx; // error: reference to host variable
const int *val4 = &xxx; // error: address of host variable
const float val5 = www; // OK except when the Microsoft compiler is used as
// the host compiler.
}
const int zzz = 20;
F.3.14.Deprecation Annotation
nvcc supports the use of deprecated attribute when using gcc, clang, xlC, icc
or pgcc host compilers, and the use of deprecated declspec when using the cl.exe
host compiler. It also supports the [[deprecated]] standard attribute when the C+
+14 dialect has been enabled. The CUDA frontend compiler will generate a deprecation
diagnostic for a reference to a deprecated entity from within the body of a __device__,
__global__ or __host__ __device__ function when __CUDA_ARCH__ is defined
(i.e., during device compilation phase). Other references to deprecated entities will be
handled by the host compiler, e.g., a reference from within a __host__ function.
The CUDA frontend compiler does not support the #pragma gcc diagnostic or
#pragma warning mechanisms supported by various host compilers. Therefore,
deprecation diagnostics generated by the CUDA frontend compiler are not affected
by these pragmas, but diagnostics generated by the host compiler will be affected. The
nvcc flag -Wno-deprecated-declarations can be used to suppress all deprecation
warnings, and the flag -Werror=deprecated-declarations can be used to turn
deprecation warnings into errors.
F.3.15.C++11 Features
C++11 features that are enabled by default by the host compiler are also supported
by nvcc, subject to the restrictions described in this document. In addition, invoking
nvcc with -std=c++11 flag turns on all C++11 features and also invokes the host
preprocessor, compiler and linker with the corresponding C++11 dialect option 11.
11 At present, the -std=c++11 flag is supported only for the following host compilers : gcc version >= 4.7, clang, icc >= 15
(without extended lambda), and xlc >= 13.1
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F.3.15.1.Lambda Expressions
The execution space specifiers for all member functions12 of the closure class associated
with a lambda expression are derived by the compiler as follows. As described in
the C++11 standard, the compiler creates a closure type in the smallest block scope,
class scope or namespace scope that contains the lambda expression. The innermost
function scope enclosing the closure type is computed, and the corresponding function's
execution space specifiers are assigned to the closure class member functions. If there is
no enclosing function scope, the execution space specifier is __host__.
Examples of lambda expressions and computed execution space specifiers are shown
below (in comments).
auto globalVar = [] { return 0; }; // __host__
void f1(void) {
auto l1 = [] { return 1; }; // __host__
}
__device__ void f2(void) {
auto l2 = [] { return 2; }; // __device__
}
__host__ __device__ void f3(void) {
auto l3 = [] { return 3; }; // __host__ __device__
}
__device__ void f4(int (*fp)() = [] { return 4; } /* __host__ */) {
}
__global__ void f5(void) {
auto l5 = [] { return 5; }; // __device__
}
__device__ void f6(void) {
struct S1_t {
static void helper(int (*fp)() = [] {return 6; } /* __device__ */) {
}
};
}
The closure type of a lambda expression cannot be used in the type or non-type
argument of a __global__ function template instantiation, unless the lambda is defined
within a __device__ or __global__ function.
12 including operator()
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Example:
template <typename T>
__global__ void foo(T in) { };
template <typename T>
struct S1_t { };
void bar(void) {
auto temp1 = [] { };
foo<<<1,1>>>(temp1); // error: lambda closure type used in
// template type argument
foo<<<1,1>>>( S1_t<decltype(temp1)>()); // error: lambda closure type used in
// template type argument
}
F.3.15.2.std::initializer_list
By default, the CUDA compiler will implicitly consider the member functions of
std::initializer_list to have __host__ __device__ execution space specifiers,
and therefore they can be invoked directly from device code. The nvcc flag --no-
host-device-initializer-list will disable this behavior; member functions of
std::initializer_list will then be considered as __host__ functions and will not
be directly invokable from device code.
Example:
#include <initializer_list>
__device__ int foo(std::initializer_list<int> in);
__device__ void bar(void)
{
foo({4,5,6}); // (a) initializer list containing only
// constant expressions.
int i = 4;
foo({i,5,6}); // (b) initializer list with at least one
// non-constant element.
// This form may have better performance than (a).
}
F.3.15.3.Rvalue references
By default, the CUDA compiler will implicitly consider std::move and std::forward
function templates to have __host__ __device__ execution space specifiers, and
therefore they can be invoked directly from device code. The nvcc flag --no-host-
device-move-forward will disable this behavior; std::move and std::forward
will then be considered as __host__ functions and will not be directly invokable from
device code.
F.3.15.4.Constexpr functions and function templates
By default, a constexpr function cannot be called from a function with incompatible
execution space 13. The experimental nvcc flag --expt-relaxed-constexpr removes
13 The restrictions are the same as with a non-constexpr callee function.
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this restriction. When this flag is specified, host code can invoke a __device__
constexpr function and device code can invoke a __host__ constexpr function. nvcc
will define the macro __CUDACC_RELAXED_CONSTEXPR__ when --expt-relaxed-
constexpr has been specified. Note that a function template instantiation may not be
a constexpr function even if the corresponding template is marked with the keyword
constexpr (C++11 Standard Section [dcl.constexpr.p6]).
F.3.15.5.Constexpr variables
Let 'V' denote a namespace scope variable or a class static member variable that has
been marked constexpr and that does not have execution space annotations (e.g.,
__device__, __constant__, __shared__). V is considered to be a host code
variable.
If V is of scalar type 14 other than long double and the type is not volatile-qualified,
the value of V can be directly used in device code. In addition, if V is of a non-scalar
type then scalar elements of V can be used inside a constexpr __device__ or __host__
__device__ function, if the call to the function is a constant expression 15. Device source
code cannot contain a reference to V or take the address of V.
Example:
constexpr int xxx = 10;
constexpr int yyy = xxx + 4;
struct S1_t { static constexpr int qqq = 100; };
constexpr int host_arr[] = { 1, 2, 3};
constexpr __device__ int get(int idx) { return host_arr[idx]; }
__device__ int foo(int idx) {
int v1 = xxx + yyy + S1_t::qqq; // OK
const int &v2 = xxx; // error: reference to host constexpr
// variable
const int *v3 = &xxx; // error: address of host constexpr
// variable
const int &v4 = S1_t::qqq; // error: reference to host constexpr
// variable
const int *v5 = &S1_t::qqq; // error: address of host constexpr
// variable
v1 += get(2); // OK: 'get(2)' is a constant
// expression.
v1 += get(idx); // error: 'get(idx)' is not a constant
// expression
v1 += host_arr[2]; // error: 'host_arr' does not have
// scalar type.
return v1;
}
F.3.15.6.Inline namespaces
For an input CUDA translation unit, the CUDA compiler may invoke the host compiler
for compiling the host code within the translation unit. In the code passed to the host
compiler, the CUDA compiler will inject additional compiler generated code, if the input
CUDA translation unit contained a definition of any of the following entities:
14 C++ Standard Section [basic.types]
15 C++ Standard Section [expr.const]
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‣__global__ function or function template instantiation
‣__device__, __constant__
‣variables with surface or texture type
The compiler generated code contains a reference to the defined entity. If the entity
is defined within an inline namespace and another entity of the same name and type
signature is defined in an enclosing namespace, this reference may be considered
ambiguous by the host compiler and host compilation will fail.
This limitation can be avoided by using unique names for such entities defined within
an inline namespace.
Example:
__device__ int Gvar;
inline namespace N1 {
__device__ int Gvar;
}
// <-- CUDA compiler inserts a reference to "Gvar" at this point in the
// translation unit. This reference will be considered ambiguous by the
// host compiler and compilation will fail.
Example:
inline namespace N1 {
namespace N2 {
__device__ int Gvar;
}
}
namespace N2 {
__device__ int Gvar;
}
// <-- CUDA compiler inserts reference to "::N2::Gvar" at this point in
// the translation unit. This reference will be considered ambiguous by
// the host compiler and compilation will fail.
F.3.15.6.1.Inline unnamed namespaces
The following entities cannot be declared in namespace scope within an inline unnamed
namespace:
‣__device__, __shared__ and __constant__ variables
‣__global__ function and function templates
‣variables with surface or texture type
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Example:
inline namespace {
namespace N2 {
template <typename T>
__global__ void foo(void); // error
__global__ void bar(void) { } // error
template <>
__global__ void foo<int>(void) { } // error
__device__ int x1b; // error
__constant__ int x2b; // error
__shared__ int x3b; // error
texture<int> q2; // error
surface<int> s2; // error
}
};
F.3.15.7.thread_local
The thread_local storage specifier is not allowed in device code.
F.3.15.8.__global__ functions and function templates
If the closure type associated with a lambda expression is used in a template argument
of a __global__ function template instantiation, the lambda expression must either
be defined in the immediate or nested block scope of a __device__ or __global__
function, or must be an extended lambda.
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Example:
template <typename T>
__global__ void kernel(T in) { }
__device__ void foo_device(void)
{
// All kernel instantiations in this function
// are valid, since the lambdas are defined inside
// a __device__ function.
kernel<<<1,1>>>( [] __device__ { } );
kernel<<<1,1>>>( [] __host__ __device__ { } );
kernel<<<1,1>>>( [] { } );
}
auto lam1 = [] { };
auto lam2 = [] __host__ __device__ { };
void foo_host(void)
{
// OK: instantiated with closure type of an extended __device__ lambda
kernel<<<1,1>>>( [] __device__ { } );
// OK: instantiated with closure type of an extended __host__ __device__
// lambda
kernel<<<1,1>>>( [] __host__ __device__ { } );
// error: unsupported: instantiated with closure type of a lambda
// that is not an extended lambda
kernel<<<1,1>>>( [] { } );
// error: unsupported: instantiated with closure type of a lambda
// that is not an extended lambda
kernel<<<1,1>>>( lam1);
// error: unsupported: instantiated with closure type of a lambda
// that is not an extended lambda
kernel<<<1,1>>>( lam2);
}
A __global__ function or function template cannot be declared as constexpr and
cannot have trailing return type.
The following types are not allowed for a parameter of a __global__ function or
function template:
‣rvalue reference type
‣std::initializer_list
A variadic __global__ function template has the following restrictions:
‣Only a single pack parameter is allowed.
‣The pack parameter must be listed last in the template parameter list.
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Example:
// ok
template <template <typename...> class Wrapper, typename... Pack>
__global__ void foo1(Wrapper<Pack...>);
// error: pack parameter is not last in parameter list
template <typename... Pack, template <typename...> class Wrapper>
__global__ void foo2(Wrapper<Pack...>);
// error: multiple parameter packs
template <typename... Pack1, int...Pack2, template<typename...> class Wrapper1,
template<int...> class Wrapper2>
__global__ void foo3(Wrapper1<Pack1...>, Wrapper2<Pack2...>);
F.3.15.9.__device__/__constant__/__shared__ variables
__device__, __constant__ and __shared__ variables cannot be marked with the
keyword constexpr and cannot have rvalue reference type.
F.3.15.10.Defaulted functions
Execution space specifiers on a defaulted function are ignored by the CUDA compiler.
Example:
struct S1 {
// warning: __host__ annotation on a defaulted function is ignored
__host__ S1() = default;
};
struct S2 {
// warning: __device__ annotation on a defaulted function is ignored
__device__ ~S2() = default;
};
F.3.16.C++14 Features
C++14 features enabled by default by the host compiler are also supported by nvcc.
Passing nvcc -std=c++14 flag turns on all C++14 features and also invokes the host
preprocessor, compiler and linker with the corresponding C++14 dialect option 16. This
section describes the restrictions on the supported C++14 features.
F.3.16.1.Functions with deduced return type
If a __device__ function has deduced return type, the CUDA frontend compiler
will change the function declaration to have a void return type, before invoking the
host compiler. This may cause issues for introspecting the deduced return type of the
__device__ function in host code. Thus, the CUDA compiler will issue compile-time
errors for referencing such deduced return type outside device function bodies, except if
the reference is absent when __CUDA_ARCH__ is undefined.
16 At present, the -std=c++14 flag is supported only for the following host compilers : gcc version >= 5.1 and clang
version >= 3.7
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Examples:
__device__ auto fn1(int x) {
return x;
}
__device__ decltype(auto) fn2(int x) {
return x;
}
__device__ void device_fn1() {
// OK
int (*p1)(int) = fn1;
}
// error: referenced outside device function bodies
decltype(fn1(10)) g1;
void host_fn1() {
// error: referenced outside device function bodies
int (*p1)(int) = fn1;
struct S_local_t {
// error: referenced outside device function bodies
decltype(fn2(10)) m1;
S_local_t() : m1(10) { }
};
}
// error: referenced outside device function bodies
template <typename T = decltype(fn2)>
void host_fn2() { }
template<typename T> struct S1_t { };
// error: referenced outside device function bodies
struct S1_derived_t : S1_t<decltype(fn1)> { };
F.3.16.2.Variable templates
A __device__/__constant__ variable template cannot have a const qualified type on
Windows.
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Examples:
// error: a __device__ variable template cannot
// have a const qualified type on Windows
template <typename T>
__device__ const T d1(2);
int *const x = nullptr;
// error: a __device__ variable template cannot
// have a const qualified type on Windows
template <typename T>
__device__ T *const d2(x);
// OK
template <typename T>
__device__ const T *d3;
__device__ void fn() {
int t1 = d1<int>;
int *const t2 = d2<int>;
const int *t3 = d3<int>;
}
F.3.16.3.[[deprecated]] attribute
The CUDA frontend compiler accepts the [[deprecated]] attribute, and regenerates
it in the code sent to the host compiler. However, the CUDA frontend compiler will not
emit any warnings on the uses of this attribute.
F.4.Polymorphic Function Wrappers
A polymorphic function wrapper class template nvstd::function is provided in the
nvfunctional header. Instances of this class template can be used to store, copy and
invoke any callable target, e.g., lambda expressions. nvstd::function can be used in
both host and device code.
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Example:
#include <nvfunctional>
__device__ int foo_d() { return 1; }
__host__ __device__ int foo_hd () { return 2; }
__host__ int foo_h() { return 3; }
__global__ void kernel(int *result) {
nvstd::function<int()> fn1 = foo_d;
nvstd::function<int()> fn2 = foo_hd;
nvstd::function<int()> fn3 = []() { return 10; };
*result = fn1() + fn2() + fn3();
}
__host__ __device__ void hostdevice_func(int *result) {
nvstd::function<int()> fn1 = foo_hd;
nvstd::function<int()> fn2 = []() { return 10; };
*result = fn1() + fn2();
}
__host__ void host_func(int *result) {
nvstd::function<int()> fn1 = foo_h;
nvstd::function<int()> fn2 = foo_hd;
nvstd::function<int()> fn3 = []() { return 10; };
*result = fn1() + fn2() + fn3();
}
Instances of nvstd::function in host code cannot be initialized with the address of a
__device__ function or with a functor whose operator() is a __device__ function.
Instances of nvstd::function in device code cannot be initialized with the address of
a __host__ function or with a functor whose operator() is a __host__ function.
nvstd::function instances cannot be passed from host code to device code (and
vice versa) at run time. nvstd::function cannot be used in the parameter type of a
__global__ function, if the __global__ function is launched from host code.
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Example:
#include <nvfunctional>
__device__ int foo_d() { return 1; }
__host__ int foo_h() { return 3; }
auto lam_h = [] { return 0; };
__global__ void k(void) {
// error: initialized with address of __host__ function
nvstd::function<int()> fn1 = foo_h;
// error: initialized with address of functor with
// __host__ operator() function
nvstd::function<int()> fn2 = lam_h;
}
__global__ void kern(nvstd::function<int()> f1) { }
void foo(void) {
// error: initialized with address of __device__ function
nvstd::function<int()> fn1 = foo_d;
auto lam_d = [=] __device__ { return 1; };
// error: initialized with address of functor with
// __device__ operator() function
nvstd::function<int()> fn2 = lam_d;
// error: passing nvstd::function from host to device
kern<<<1,1>>>(fn2);
}
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nvstd::function is defined in the nvfunctional header as follows:
namespace nvstd {
template <class _RetType, class ..._ArgTypes>
class function<_RetType(_ArgTypes...)>
{
public:
// constructors
__device__ __host__ function() noexcept;
__device__ __host__ function(nullptr_t) noexcept;
__device__ __host__ function(const function &);
__device__ __host__ function(function &&);
template<class _F>
__device__ __host__ function(_F);
// destructor
__device__ __host__ ~function();
// assignment operators
__device__ __host__ function& operator=(const function&);
__device__ __host__ function& operator=(function&&);
__device__ __host__ function& operator=(nullptr_t);
__device__ __host__ function& operator=(_F&&);
// swap
__device__ __host__ void swap(function&) noexcept;
// function capacity
__device__ __host__ explicit operator bool() const noexcept;
// function invocation
__device__ _RetType operator()(_ArgTypes...) const;
};
// null pointer comparisons
template <class _R, class... _ArgTypes>
__device__ __host__
bool operator==(const function<_R(_ArgTypes...)>&, nullptr_t) noexcept;
template <class _R, class... _ArgTypes>
__device__ __host__
bool operator==(nullptr_t, const function<_R(_ArgTypes...)>&) noexcept;
template <class _R, class... _ArgTypes>
__device__ __host__
bool operator!=(const function<_R(_ArgTypes...)>&, nullptr_t) noexcept;
template <class _R, class... _ArgTypes>
__device__ __host__
bool operator!=(nullptr_t, const function<_R(_ArgTypes...)>&) noexcept;
// specialized algorithms
template <class _R, class... _ArgTypes>
__device__ __host__
void swap(function<_R(_ArgTypes...)>&, function<_R(_ArgTypes...)>&);
}
F.5.Experimental Feature: Extended Lambdas
The nvcc flag '--expt-extended-lambda' allows explicit execution space annotations
in a lambda expression. The execution space annotations should be present after the
'lambda-introducer' and before the optional 'lambda-declarator'. nvcc will define the
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macro __CUDACC_EXTENDED_LAMBDA__ when the '--expt-extended-lambda' flag
has been specified.
An 'extended __device__ lambda' is a lambda expression that is annotated explicitly
with '__device__', and is defined within the immediate or nested block scope of a
__host__ or __host__ __device__ function.
An 'extended __host__ __device__ lambda' is a lambda expression that is annotated
explicitly with both '__host__' and '__device__', and is defined within the immediate
or nested block scope of a __host__ or __host__ __device__ function.
An 'extended lambda' denotes either an extended __device__ lambda or an extended
__host__ __device__ lambda. Extended lambdas can be used in the type arguments
of __global__ function template instantiation.
If the execution space annotations are not explicitly specified, they are computed based
on the scopes enclosing the closure class associated with the lambda, as described in the
section on C++11 support. The execution space annotations are applied to all methods of
the closure class associated with the lambda.
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Example:
void foo_host(void)
{
// not an extended lambda: no explicit execution space annotations
auto lam1 = [] { };
// extended __device__ lambda
auto lam2 = [] __device__ { };
// extended __host__ __device__ lambda
auto lam3 = [] __host__ __device__ { };
// not an extended lambda: explicitly annotated with only '__host__'
auto lam4 = [] __host__ { };
}
__host__ __device__ void foo_host_device(void)
{
// not an extended lambda: no explicit execution space annotations
auto lam1 = [] { };
// extended __device__ lambda
auto lam2 = [] __device__ { };
// extended __host__ __device__ lambda
auto lam3 = [] __host__ __device__ { };
// not an extended lambda: explicitly annotated with only '__host__'
auto lam4 = [] __host__ { };
}
__device__ void foo_device(void)
{
// none of the lambdas within this function are extended lambdas,
// because the enclosing function is not a __host__ or __host__ __device__
// function.
auto lam1 = [] { };
auto lam2 = [] __device__ { };
auto lam3 = [] __host__ __device__ { };
auto lam4 = [] __host__ { };
}
// lam1 and lam2 are not extended lambdas because they are not defined
// within a __host__ or __host__ __device__ function.
auto lam1 = [] { };
auto lam2 = [] __host__ __device__ { };
F.5.1.Extended Lambda Type Traits
The compiler provides type traits to detect closure types for extended lambdas at
compile time:
__nv_is_extended_device_lambda_closure_type(type): If 'type' is the closure
class created for an extended __device__ lambda, then the trait is true, otherwise it is
false.
__nv_is_extended_host_device_lambda_closure_type(type): If 'type' is the
closure class created for an extended __host__ __device__ lambda, then the trait is
true, otherwise it is false.
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These traits can be used in all compilation modes, irrespective of whether lambdas or
extended lambdas are enabled17.
Example:
#define IS_D_LAMBDA(X) __nv_is_extended_device_lambda_closure_type(X)
#define IS_HD_LAMBDA(X) __nv_is_extended_host_device_lambda_closure_type(X)
auto lam0 = [] __host__ __device__ { };
void foo(void) {
auto lam1 = [] { };
auto lam2 = [] __device__ { };
auto lam3 = [] __host__ __device__ { };
// lam0 is not an extended lambda (since defined outside function scope)
static_assert(!IS_D_LAMBDA(decltype(lam0)), "");
static_assert(!IS_HD_LAMBDA(decltype(lam0)), "");
// lam1 is not an extended lambda (since no execution space annotations)
static_assert(!IS_D_LAMBDA(decltype(lam1)), "");
static_assert(!IS_HD_LAMBDA(decltype(lam1)), "");
// lam2 is an extended __device__ lambda
static_assert(IS_D_LAMBDA(decltype(lam2)), "");
static_assert(!IS_HD_LAMBDA(decltype(lam2)), "");
// lam3 is an extended __host__ __device__ lambda
static_assert(!IS_D_LAMBDA(decltype(lam3)), "");
static_assert(IS_HD_LAMBDA(decltype(lam3)), "");
}
F.5.2.Extended Lambda Restrictions
The CUDA compiler will replace an extended lambda expression with an instance of a
placeholder type defined in namespace scope, before invoking the host compiler. The
template argument of the placeholder type requires taking the address of a function
enclosing the original extended lambda expression. This is required for the correct
execution of any __global__ function template whose template argument involves
the closure type of an extended lambda. The enclosing function is computed as follows.
By definition, the extended lambda is present within the immediate or nested block
scope of a __host__ or __host__ __device__ function. If this function is not the
operator() of a lambda expression, then it is considered the enclosing function for the
extended lambda. Otherwise, the extended lambda is defined within the immediate or
nested block scope of the operator() of one or more enclosing lambda expressions. If
the outermost such lambda expression is defined in the immediate or nested block scope
of a function F, then F is the computed enclosing function, else the enclosing function
does not exist.
17 The traits will always return false if extended lambda mode is not active.
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Example:
void foo(void) {
// enclosing function for lam1 is "foo"
auto lam1 = [] __device__ { };
auto lam2 = [] {
auto lam3 = [] {
// enclosing function for lam4 is "foo"
auto lam4 = [] __host__ __device__ { };
};
};
}
auto lam6 = [] {
// enclosing function for lam7 does not exist
auto lam7 = [] __host__ __device__ { };
};
Here are the restrictions on extended lambdas:
1. An extended lambda cannot be defined inside another extended lambda expression.
Example:
void foo(void) {
auto lam1 = [] __host__ __device__ {
// error: extended lambda defined within another extended lambda
auto lam2 = [] __host__ __device__ { };
};
2. An extended lambda cannot be defined inside a generic lambda expression.
Example:
void foo(void) {
auto lam1 = [] (auto) {
// error: extended lambda defined within a generic lambda
auto lam2 = [] __host__ __device__ { };
};
3. If an extended lambda is defined within the immediate or nested block scope of one
or more nested lambda expression, the outermost such lambda expression must be
defined inside the immediate or nested block scope of a function.
Example:
auto lam1 = [] {
// error: outer enclosing lambda is not defined within a
// non-lambda-operator() function.
auto lam2 = [] __host__ __device__ { };
};
4. The enclosing function for the extended lambda must be named and its address can
be taken. If the enclosing function is a class member, then the following conditions
must be satisfied:
‣All classes enclosing the member function must have a name.
‣The member function must not have private or protected access within its
parent class.
‣All enclosing classes must not have private or protected access within their
respective parent classes.
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Example:
void foo(void) {
// OK
auto lam1 = [] __device__ { return 0; };
{
// OK
auto lam2 = [] __device__ { return 0; };
// OK
auto lam3 = [] __device__ __host__ { return 0; };
}
}
struct S1_t {
S1_t(void) {
// Error: cannot take address of enclosing function
auto lam4 = [] __device__ { return 0; };
}
};
class C0_t {
void foo(void) {
// Error: enclosing function has private access in parent class
auto temp1 = [] __device__ { return 10; };
}
struct S2_t {
void foo(void) {
// Error: enclosing class S2_t has private access in its
// parent class
auto temp1 = [] __device__ { return 10; };
}
};
};
5. An extended lambda cannot be defined in a class that is local to a function.
Example:
void foo(void) {
struct S1_t {
void bar(void) {
// Error: bar is member of a class that is local to a function.
auto lam4 = [] __host__ __device__ { return 0; };
}
};
}
6. The enclosing function for an extended lambda cannot have deduced return type.
Example:
auto foo(void) {
// Error: the return type of foo is deduced.
auto lam1 = [] __host__ __device__ { return 0; };
}
7. __host__ __device__ extended lambdas cannot be generic lambdas.
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Example:
void foo(void) {
// Error: __host__ __device__ extended lambdas cannot be
// generic lambdas.
auto lam1 = [] __host__ __device__ (auto i) { return i; };
// Error: __host__ __device__ extended lambdas cannot be
// generic lambdas.
auto lam2 = [] __host__ __device__ (auto ...i) {
return sizeof...(i);
};
}
8. If the enclosing function is an instantiation of a function template or a member
function template, and/or the function is a member of a class template, the
template(s) must satisfy the following constraints:
‣The template must have at most one variadic parameter, and it must be listed
last in the template parameter list.
‣The template parameters must be named.
‣The template instantiation argument types cannot involve types that are either
local to a function (except for closure types for extended lambdas), or are private
or protected class members.
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Example:
template <typename T>
__global__ void kern(T in) { in(); }
template <typename... T>
struct foo {};
template < template <typename...> class T, typename... P1,
typename... P2>
void
bar1(const T<P1...>, const T<P2...>)
{
// Error: enclosing function has multiple parameter packs
auto lam1 = [] __device__ { return 10; };
}
template < template <typename...> class T, typename... P1,
typename T2>
void
bar2(const T<P1...>, T2)
{
// Error: for enclosing function, the
// parameter pack is not last in the template parameter list.
auto lam1 = [] __device__ { return 10; };
}
template <typename T, T>
void bar3(void)
{
// Error: for enclosing function, the second template
// parameter is not named.
auto lam1 = [] __device__ { return 10; };
}
int main()
{
foo<char, int, float> f1;
foo<char, int> f2;
bar1(f1, f2);
bar2(f1, 10);
bar3<int, 10>();
}
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Example:
template <typename T>
__global__ void kern(T in) { in(); }
template <typename T>
void bar4(void)
{
auto lam1 = [] __device__ { return 10; };
kern<<<1,1>>>(lam1);
}
struct C1_t { struct S1_t { }; friend int main(void); };
int main()
{
struct S1_t { };
// Error: enclosing function for device lambda in bar4
// is instantiated with a type local to main.
bar4<S1_t>();
// Error: enclosing function for device lambda in bar4
// is instantiated with a type that is a private member
// of a class.
bar4<C1_t::S1_t>();
}
9. With Visual Studio 2013 and later Visual Studio host compilers, the enclosing
function must have external linkage. The restriction is present because this host
compiler does not support using the address of non-extern linkage functions as
template arguments, which is needed by the CUDA compiler transformations to
support extended lambdas.
10. An extended lambda has the following restrictions on captured variables:
‣Variables can only be captured by value.
‣Variables of array type cannot be captured.
‣A function parameter that is an element of a variadic argument pack cannot be
captured.
‣The type of the captured variable cannot involve types that are either local to
a function (except for closure types of extended lambdas), or are private or
protected class members.
‣For a __host__ __device__ extended lambda, the types used in the return or
parameter types of the lambda expression's operator() cannot involve types
that are either local to a function (except for closure types of extended lambdas),
or are private or protected class members.
‣Init-capture is not supported for __host__ __device__ extended lambdas. Init-
capture is supported for __device__ extended lambdas, except when the init-
capture is of type std::initializer_list.
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Example
void foo(void) {
// OK: an init-capture is allowed for an
// extended __device__ lambda.
auto lam1 = [x = 1] __device__ () { return x; };
// Error: an init-capture is not allowed for
// an extended __host__ __device__ lambda.
auto lam2 = [x = 1] __host__ __device__ () { return x; };
int a = 1;
// Error: an extended __device__ lambda cannot capture
// variables by reference.
auto lam3 = [&a] __device__ () { return a; };
// Error: by-reference capture is not allowed
// for an extended __device__ lambda.
auto lam4 = [&x = a] __device__ () { return x; };
int b[10] = {0};
// Error: an extended __device__ lambda cannot capture a variable
// with array type.
auto lam5 = [b] __device__ () { return b[0]; };
struct S1_t { };
S1_t s1;
// Error: a type local to a function cannot be used in the type
// of a captured variable.
auto lam6 = [s1] __device__ () { };
// Error: an init-capture cannot be of type std::initializer_list.
auto lam7 = [x = {11}] __device__ () { };
std::initializer_list<int> b = {11,22,33};
// Error: an init-capture cannot be of type std::initializer_list.
auto lam8 = [x = b] __device__ () { };
}
11. When parsing a function, the CUDA compiler assigns a counter value to each
extended lambda within that function. This counter value is used in the substituted
named type passed to the host compiler. Hence, whether or not an extended
lambda is defined within a function should not depend on a particular value of
__CUDA_ARCH__, or on __CUDA_ARCH__ being undefined.
Example
template <typename T>
__global__ void kernel(T in) { in(); }
__host__ __device__ void foo(void)
{
// Error: the number and relative declaration
// order of extended lambdas depends on
// __CUDA_ARCH__
#if defined(__CUDA_ARCH__)
auto lam1 = [] __device__ { return 0; };
auto lam1b = [] __host___ __device__ { return 10; };
#endif
auto lam2 = [] __device__ { return 4; };
kernel<<<1,1>>>(lam2);
}
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12. As described above, the CUDA compiler replaces a __device__ extended lambda
defined in a host function with a placeholder type defined in namespace scope. This
placeholder type does not define a operator() function equivalent to the original
lambda declaration. An attempt to determine the return type or parameter types of
the operator() function may therefore work incorrectly in host code, as the code
processed by the host compiler will be semantically different than the input code
processed by the CUDA compiler. However, it is ok to introspect the return type
or parameter types of the operator() function within device code. Note that this
restriction does not apply to __host__ __device__ extended lambdas.
Example
#include <type_traits>
void foo(void)
{
auto lam1 = [] __device__ { return 10; };
// Error: attempt to extract the return type
// of a __device__ lambda in host code
std::result_of<decltype(lam1)()>::type xx1 = 1;
auto lam2 = [] __host__ __device__ { return 10; };
// OK : lam2 represents a __host__ __device__ extended lambda
std::result_of<decltype(lam2)()>::type xx2 = 1;
}
13. If the functor object represented by an extended lambda is passed from host to
device code (e.g., as the argument of a __global__ function), then any expression
in the body of the lambda expression that captures variables must be remain
unchanged irrespective of whether the __CUDA_ARCH__ macro is defined, and
whether the macro has a particular value. This restriction arises because the
lambda's closure class layout depends on the order in which captured variables are
encountered when the compiler processes the lambda expression; the program may
execute incorrectly if the closure class layout differs in device and host compilation.
Example
__device__ int result;
template <typename T>
__global__ void kernel(T in) { result = in(); }
void foo(void) {
int x1 = 1;
auto lam1 = [=] __host__ __device__ {
// Error: "x1" is only captured when __CUDA_ARCH__ is defined.
#ifdef __CUDA_ARCH__
return x1 + 1;
#else
return 10;
#endif
};
kernel<<<1,1>>>(lam1);
}
14. As described previously, the CUDA compiler replaces an extended __device__
lambda expression with an instance of a placeholder type in the code sent to the host
compiler. This placeholder type does not define a pointer-to-function conversion
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operator in host code, however the conversion operator is provided in device code.
Note that this restriction does not apply to __host__ __device__ extended
lambdas.
Example
template <typename T>
__global__ void kern(T in)
{
int (*fp)(double) = in;
// OK: conversion in device code is supported
fp(0);
auto lam1 = [](double) { return 1; };
// OK: conversion in device code is supported
fp = lam1;
fp(0);
}
void foo(void)
{
auto lam_d = [] __device__ (double) { return 1; };
auto lam_hd = [] __host__ __device__ (double) { return 1; };
kern<<<1,1>>>(lam_d);
kern<<<1,1>>>(lam_hd);
// OK : conversion for __host__ __device__ lambda is supported
// in host code
int (*fp)(double) = lam_hd;
// Error: conversion for __device__ lambda is not supported in
// host code.
int (*fp2)(double) = lam_d;
}
The CUDA compiler will generate compiler diagnostics for a subset of cases described
in 1-10; no diagnostic will be generated for cases 11-14, but the host compiler may fail to
compile the generated code.
F.5.3.Notes on __host__ __device__ lambdas
Unlike __device__ lambdas, __host__ __device__ lambdas can be called from host
code. As described earlier, the CUDA compiler replaces an extended lambda expression
defined in host code with an instance of a named placeholder type. The placeholder
type for an extended __host__ __device__ lambda invokes the orignal lambda's
operator() with an indirect function call 18.
The presence of the indirect function call may cause an extended __host__
__device__ lambda to be less optimized by the host compiler than lambdas that
are implicitly or explicitly __host__ only. In the latter case, the host compiler can
easily inline the body of the lambda into the calling context. But in case of an extended
__host__ __device__ lambda, the host compiler encounters the indirect function call
and may not be able to easily inline the original __host__ __device__ lambda body.
18 The closure object is stored in a type-elided container similar to std::function.
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F.5.4.*this Capture By Value
When a lambda is defined within a non-static class member function, and the body of
the lambda refers to a class member variable, C++11/C++14 rules require that the this
pointer of the class is captured by value, instead of the referenced member variable.
If the lambda is an extended __device__ or __host__ __device__ lambda defined
in a host function, and the lambda is executed on the GPU, accessing the referenced
member variable on the GPU will cause a run time error if the this pointer points to
host memory.
Example:
#include <cstdio>
template <typename T>
__global__ void foo(T in) { printf("\n value = %d", in()); }
struct S1_t {
int xxx;
__host__ __device__ S1_t(void) : xxx(10) { };
void doit(void) {
auto lam1 = [=] __device__ {
// reference to "xxx" causes
// the 'this' pointer (S1_t*) to be captured by value
return xxx + 1;
};
// Kernel launch fails at run time because 'this->xxx'
// is not accessible from the GPU
foo<<<1,1>>>(lam1);
cudaDeviceSynchronize();
}
};
int main(void) {
S1_t s1;
s1.doit();
}
C++17 solves this problem by adding a new "*this" capture mode. In this mode, the
compiler makes a copy of the object denoted by "*this" instead of capturing the pointer
this by value. The "*this" capture mode is described in more detail here: http://
www.open-std.org/jtc1/sc22/wg21/docs/papers/2016/p0018r3.html .
The CUDA compiler supports the "*this" capture mode for lambdas defined within
__device__ and __global__ functions and for extended __device__ lambdas
defined in host code, when the --expt-extended-lambda nvcc flag is used.
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Here's the above example modified to use "*this" capture mode:
#include <cstdio>
template <typename T>
__global__ void foo(T in) { printf("\n value = %d", in()); }
struct S1_t {
int xxx;
__host__ __device__ S1_t(void) : xxx(10) { };
void doit(void) {
// note the "*this" capture specification
auto lam1 = [=, *this] __device__ {
// reference to "xxx" causes
// the object denoted by '*this' to be captured by
// value, and the GPU code will access copy_of_star_this->xxx
return xxx + 1;
};
// Kernel launch succeeds
foo<<<1,1>>>(lam1);
cudaDeviceSynchronize();
}
};
int main(void) {
S1_t s1;
s1.doit();
}
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"*this" capture mode is not allowed for unannotated lambdas defined in host code, or
for extended __host__ __device__ lambdas. Examples of supported and unsupported
usage:
struct S1_t {
int xxx;
__host__ __device__ S1_t(void) : xxx(10) { };
void host_func(void) {
// OK: use in an extended __device__ lambda
auto lam1 = [=, *this] __device__ { return xxx; };
// Error: use in an extended __host__ __device__ lambda
auto lam2 = [=, *this] __host__ __device__ { return xxx; };
// Error: use in an unannotated lambda in host function
auto lam3 = [=, *this] { return xxx; };
}
__device__ void device_func(void) {
// OK: use in a lambda defined in a __device__ function
auto lam1 = [=, *this] __device__ { return xxx; };
// OK: use in a lambda defined in a __device__ function
auto lam2 = [=, *this] __host__ __device__ { return xxx; };
// OK: use in a lambda defined in a __device__ function
auto lam3 = [=, *this] { return xxx; };
}
__host__ __device__ void host_device_func(void) {
// OK: use in an extended __device__ lambda
auto lam1 = [=, *this] __device__ { return xxx; };
// Error: use in an extended __host__ __device__ lambda
auto lam2 = [=, *this] __host__ __device__ { return xxx; };
// Error: use in an unannotated lambda in a __host__ __device__ function
auto lam3 = [=, *this] { return xxx; };
}
};
F.5.5.Additional Notes
1. ADL Lookup: As described earlier, the CUDA compiler will replace an extended
lambda expression with an instance of a placeholder type, before invoking the
host compiler. One template argument of the placeholder type uses the address of
the function enclosing the original lambda expression. This may cause additional
namespaces to participate in argument dependent lookup (ADL), for any host
function call whose argument types involve the closure type of the extended lambda
expression. This may cause an incorrect function to be selected by the host compiler.
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Example:
namespace N1 {
struct S1_t { };
template <typename T> void foo(T);
};
namespace N2 {
template <typename T> int foo(T);
template <typename T> void doit(T in) { foo(in); }
}
void bar(N1::S1_t in)
{
/* extended __device__ lambda. In the code sent to the host compiler,
this
is replaced with the placeholder type instantiation expression
' __nv_dl_wrapper_t< __nv_dl_tag<void (*)(N1::S1_t in),(&bar),1> > { }'
As a result, the namespace 'N1' participates in ADL lookup of the
call to "foo" in the body of N2::doit, causing ambiguity.
*/
auto lam1 = [=] __device__ { };
N2::doit(lam1);
}
In the example above, the CUDA compiler replaced the extended lambda with a
placeholder type that involves the N1 namespace. As a result, the namespace N1
participates in the ADL lookup for foo(in) in the body of N2::doit, and host
compilation fails because multiple overload candidates N1::foo and N2::foo are
found.
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F.6.Code Samples
F.6.1.Data Aggregation Class
class PixelRGBA {
public:
__device__ PixelRGBA(): r_(0), g_(0), b_(0), a_(0) { }
__device__ PixelRGBA(unsigned char r, unsigned char g,
unsigned char b, unsigned char a = 255):
r_(r), g_(g), b_(b), a_(a) { }
private:
unsigned char r_, g_, b_, a_;
friend PixelRGBA operator+(const PixelRGBA const PixelRGBA&);
};
__device__
PixelRGBA operator+(const PixelRGBA& p1, const PixelRGBA& p2)
{
return PixelRGBA(p1.r_ + p2.r_, p1.g_ + p2.g_,
p1.b_ + p2.b_, p1.a_ + p2.a_);
}
__device__ void func(void)
{
PixelRGBA p1, p2;
// ... // Initialization of p1 and p2 here
PixelRGBA p3 = p1 + p2;
}
F.6.2.Derived Class
__device__ void* operator new(size_t bytes, MemoryPool& p);
__device__ void operator delete(void*, MemoryPool& p);
class Shape {
public:
__device__ Shape(void) { }
__device__ void putThis(PrintBuffer *p) const;
__device__ virtual void Draw(PrintBuffer *p) const {
p->put("Shapeless");
}
__device__ virtual ~Shape() {}
};
class Point : public Shape {
public:
__device__ Point() : x(0), y(0) {}
__device__ Point(int ix, int iy) : x(ix), y(iy) { }
__device__ void PutCoord(PrintBuffer *p) const;
__device__ void Draw(PrintBuffer *p) const;
__device__ ~Point() {}
private:
int x, y;
};
__device__ Shape* GetPointObj(MemoryPool& pool)
{
Shape* shape = new(pool) Point(rand(-20,10), rand(-100,-20));
return shape;
}
C/C++ Language Support
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F.6.3.Class Template
template <class T>
class myValues {
T values[MAX_VALUES];
public:
__device__ myValues(T clear) { ... }
__device__ void setValue(int Idx, T value) { ... }
__device__ void putToMemory(T* valueLocation) { ... }
};
template <class T>
void __global__ useValues(T* memoryBuffer) {
myValues<T> myLocation(0);
...
}
__device__ void* buffer;
int main()
{
...
useValues<int><<<blocks, threads>>>(buffer);
...
}
F.6.4.Function Template
template <typename T>
__device__ bool func(T x)
{
...
return (...);
}
template <>
__device__ bool func<int>(T x) // Specialization
{
return true;
}
// Explicit argument specification
bool result = func<double>(0.5);
// Implicit argument deduction
int x = 1;
bool result = func(x);
C/C++ Language Support
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F.6.5.Functor Class
class Add {
public:
__device__ float operator() (float a, float b) const
{
return a + b;
}
};
class Sub {
public:
__device__ float operator() (float a, float b) const
{
return a - b;
}
};
// Device code
template<class O> __global__
void VectorOperation(const float * A, const float * B, float * C,
unsigned int N, O op)
{
unsigned int iElement = blockDim.x * blockIdx.x + threadIdx.x;
if (iElement < N)
C[iElement] = op(A[iElement], B[iElement]);
}
// Host code
int main()
{
...
VectorOperation<<<blocks, threads>>>(v1, v2, v3, N, Add());
...
}
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AppendixG.
TEXTURE FETCHING
This appendix gives the formula used to compute the value returned by the texture
functions of Texture Functions depending on the various attributes of the texture
reference (see Texture and Surface Memory).
The texture bound to the texture reference is represented as an array T of
‣N texels for a one-dimensional texture,
‣N x M texels for a two-dimensional texture,
‣N x M x L texels for a three-dimensional texture.
It is fetched using non-normalized texture coordinates x, y, and z, or the normalized
texture coordinates x/N, y/M, and z/L as described in Texture Memory. In this appendix,
the coordinates are assumed to be in the valid range. Texture Memory explained how
out-of-range coordinates are remapped to the valid range based on the addressing
mode.
G.1.Nearest-Point Sampling
In this filtering mode, the value returned by the texture fetch is
‣tex(x)=T[i] for a one-dimensional texture,
‣tex(x,y)=T[i,j] for a two-dimensional texture,
‣tex(x,y,z)=T[i,j,k] for a three-dimensional texture,
where i=floor(x), j=floor(y), and k=floor(z).
Figure 13 illustrates nearest-point sampling for a one-dimensional texture with N=4.
For integer textures, the value returned by the texture fetch can be optionally remapped
to [0.0, 1.0] (see Texture Memory).
Texture Fetching
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0
4
1
2
3
T[0]
T[1]
T[2]
T[3]
x
0
1
0.25
0.5
0.75
Non- Normalized
Normalized
tex(x)
Figure13 Nearest-Point Sampling Filtering Mode
Nearest-point sampling of a one-dimensional texture of four texels.
G.2.Linear Filtering
In this filtering mode, which is only available for floating-point textures, the value
returned by the texture fetch is
‣tex(x)=(1−α)T[i]+αT[i+1] for a one-dimensional texture,
‣tex(x,y)=(1−α)(1−β)T[i,j]+α(1−β)T[i+1,j]+(1−α)βT[i,j+1]+αβT[i+1,j+1] for a two-
dimensional texture,
‣tex(x,y,z) =
(1−α)(1−β)(1−γ)T[i,j,k]+α(1−β)(1−γ)T[i+1,j,k]+
(1−α)β(1−γ)T[i,j+1,k]+αβ(1−γ)T[i+1,j+1,k]+
(1−α)(1−β)γT[i,j,k+1]+α(1−β)γT[i+1,j,k+1]+
(1−α)βγT[i,j+1,k+1]+αβγT[i+1,j+1,k+1]
for a three-dimensional texture,
where:
‣i=floor(xB), α=frac(xB), xB=x-0.5,
‣j=floor(yB), β=frac(yB), yB=y-0.5,
‣k=floor(zB), γ=frac(zB), zB= z-0.5,
α, β, and γ are stored in 9-bit fixed point format with 8 bits of fractional value (so 1.0 is
exactly represented).
Figure 14 illustrates linear filtering of a one-dimensional texture with N=4.
Texture Fetching
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0
4
1
2
3
T[0]
T[1]
T[2]
T[3]
tex(x)
x
0
1
0.25
0.5
0.75
Non- Normalized
Normalized
Figure14 Linear Filtering Mode
Linear filtering of a one-dimensional texture of four texels in clamp addressing mode.
G.3.Table Lookup
A table lookup TL(x) where x spans the interval [0,R] can be implemented as
TL(x)=tex((N-1)/R)x+0.5) in order to ensure that TL(0)=T[0] and TL(R)=T[N-1].
Figure 15 illustrates the use of texture filtering to implement a table lookup with R=4 or
R=1 from a one-dimensional texture with N=4.
Texture Fetching
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0
4
4/ 3
8/ 3
T[0]
T[1]
T[2]
T[3]
TL(x)
x
0
1
1/ 3
2/ 3
Figure15 One-Dimensional Table Lookup Using Linear Filtering
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AppendixH.
COMPUTE CAPABILITIES
The general specifications and features of a compute device depend on its compute
capability (see Compute Capability).
Table 13 gives the features and technical specifications associated to each compute
capability.
Floating-Point Standard reviews the compliance with the IEEE floating-point standard.
Sections Compute Capability 3.x, Compute Capability 5.x, Compute Capability 6.x, and
Compute Capability 7.x give more details on the architecture of devices of compute
capability 3.x, 5.x, 6.x, and 7.x respectively.
H.1.Features and Technical Specifications
Table13 Feature Support per Compute Capability
Feature Support Compute Capability
(Unlisted features are supported for all
compute capabilities) 3.0 3.2
3.5,
3.7,
5.0,
5.2
5.3 6.x 7.x
Atomic functions operating on 32-bit integer
values in global memory (Atomic Functions)
atomicExch() operating on 32-bit floating
point values in global memory (atomicExch())
Atomic functions operating on 32-bit integer
values in shared memory (Atomic Functions)
atomicExch() operating on 32-bit floating
point values in shared memory (atomicExch())
Atomic functions operating on 64-bit integer
values in global memory (Atomic Functions)
Atomic functions operating on 64-bit integer
values in shared memory (Atomic Functions)
Yes
Compute Capabilities
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Feature Support Compute Capability
(Unlisted features are supported for all
compute capabilities) 3.0 3.2
3.5,
3.7,
5.0,
5.2
5.3 6.x 7.x
Atomic addition operating on 32-bit floating
point values in global and shared memory
(atomicAdd())
Atomic addition operating on 64-bit floating
point values in global memory and shared
memory (atomicAdd())
No Yes
Warp vote and ballot functions (Warp Vote
Functions)
__threadfence_system() (Memory Fence
Functions)
__syncthreads_count(),
__syncthreads_and(),
__syncthreads_or() (Synchronization
Functions)
Surface functions (Surface Functions)
3D grid of thread blocks
Unified Memory Programming
Yes
Funnel shift (see reference manual) No Yes
Dynamic Parallelism No Yes
Half-precision floating-point operations:
addition, subtraction, multiplication,
comparison, warp shuffle functions,
conversion
No Yes
Tensor Core No Yes
Table14 Technical Specifications per Compute Capability
Compute Capability
Technical Specifications 3.0 3.2 3.5 3.7 5.0 5.2 5.3 6.0 6.1 6.2 7.0
Maximum number of resident
grids per device (Concurrent
Kernel Execution)
16 4 32 16 128 32 16 128
Maximum dimensionality of
grid of thread blocks 3
Maximum x-dimension of a grid
of thread blocks 231-1
Maximum y- or z-dimension of
a grid of thread blocks 65535
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Compute Capability
Technical Specifications 3.0 3.2 3.5 3.7 5.0 5.2 5.3 6.0 6.1 6.2 7.0
Maximum dimensionality of
thread block 3
Maximum x- or y-dimension of
a block 1024
Maximum z-dimension of a
block 64
Maximum number of threads
per block 1024
Warp size 32
Maximum number of resident
blocks per multiprocessor 16 32
Maximum number of resident
warps per multiprocessor 64
Maximum number of resident
threads per multiprocessor 2048
Number of 32-bit registers per
multiprocessor 64 K 128
K64 K
Maximum number of 32-bit
registers per thread block 64 K 32 K 64 K 32 K 64 K 32 K 64 K
Maximum number of 32-bit
registers per thread 63 255
Maximum amount of shared
memory per multiprocessor 48 KB 112
KB
64
KB
96
KB 64 KB 96
KB
64
KB
96
KB
Maximum amount of shared
memory per thread block 48 KB 96
KB19
Number of shared memory
banks 32
Amount of local memory per
thread 512 KB
Constant memory size 64 KB
Cache working set per
multiprocessor for constant
memory
8 KB 4 KB 8 KB
Cache working set per
multiprocessor for texture
memory
Between 12 KB and 48 KB Between 24
KB and 48 KB
Between
32
and
128
KB
Maximum width for a 1D
texture reference bound to a
CUDA array
65536
19 above 48 KB requires dynamic shared memory
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Compute Capability
Technical Specifications 3.0 3.2 3.5 3.7 5.0 5.2 5.3 6.0 6.1 6.2 7.0
Maximum width for a 1D
texture reference bound to
linear memory
227
Maximum width and number of
layers for a 1D layered texture
reference
16384 x 2048
Maximum width and height for
a 2D texture reference bound
to a CUDA array
65536 x 65535
Maximum width and height for
a 2D texture reference bound
to linear memory
65000 x 65000
Maximum width and height for
a 2D texture reference bound
to a CUDA array supporting
texture gather
16384 x 16384
Maximum width, height, and
number of layers for a 2D
layered texture reference
16384 x 16384 x 2048
Maximum width, height,
and depth for a 3D texture
reference bound to a CUDA
array
4096 x 4096 x 4096
Maximum width (and height)
for a cubemap texture
reference
16384
Maximum width (and height)
and number of layers for a
cubemap layered texture
reference
16384 x 2046
Maximum number of textures
that can be bound to a kernel 256
Maximum width for a 1D
surface reference bound to a
CUDA array
65536
Maximum width and number of
layers for a 1D layered surface
reference
65536 x 2048
Maximum width and height for
a 2D surface reference bound
to a CUDA array
65536 x 32768
Maximum width, height, and
number of layers for a 2D
layered surface reference
65536 x 32768 x 2048
Maximum width, height,
and depth for a 3D surface 65536 x 32768 x 2048
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Compute Capability
Technical Specifications 3.0 3.2 3.5 3.7 5.0 5.2 5.3 6.0 6.1 6.2 7.0
reference bound to a CUDA
array
Maximum width (and height)
for a cubemap surface
reference bound to a CUDA
array
32768
Maximum width (and height)
and number of layers for a
cubemap layered surface
reference
32768 x 2046
Maximum number of surfaces
that can be bound to a kernel 16
Maximum number of
instructions per kernel 512 million
H.2.Floating-Point Standard
All compute devices follow the IEEE 754-2008 standard for binary floating-point
arithmetic with the following deviations:
‣There is no dynamically configurable rounding mode; however, most of the
operations support multiple IEEE rounding modes, exposed via device intrinsics;
‣There is no mechanism for detecting that a floating-point exception has occurred
and all operations behave as if the IEEE-754 exceptions are always masked, and
deliver the masked response as defined by IEEE-754 if there is an exceptional event;
for the same reason, while SNaN encodings are supported, they are not signaling
and are handled as quiet;
‣The result of a single-precision floating-point operation involving one or more input
NaNs is the quiet NaN of bit pattern 0x7fffffff;
‣Double-precision floating-point absolute value and negation are not compliant with
IEEE-754 with respect to NaNs; these are passed through unchanged;
Code must be compiled with -ftz=false, -prec-div=true, and -prec-sqrt=true
to ensure IEEE compliance (this is the default setting; see the nvcc user manual for
description of these compilation flags).
Regardless of the setting of the compiler flag -ftz,
‣Atomic single-precision floating-point adds on global memory always operate in
flush-to-zero mode, i.e., behave equivalent to FADD.F32.FTZ.RN,
‣Atomic single-precision floating-point adds on shared memory always operate with
denormal support, i.e., behave equivalent to FADD.F32.RN.
In accordance to the IEEE-754R standard, if one of the input parameters to fminf(),
fmin(), fmaxf(), or fmax() is NaN, but not the other, the result is the non-NaN
parameter.
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The conversion of a floating-point value to an integer value in the case where the
floating-point value falls outside the range of the integer format is left undefined by
IEEE-754. For compute devices, the behavior is to clamp to the end of the supported
range. This is unlike the x86 architecture behavior.
The behavior of integer division by zero and integer overflow is left undefined by
IEEE-754. For compute devices, there is no mechanism for detecting that such integer
operation exceptions have occurred. Integer division by zero yields an unspecified,
machine-specific value.
http://developer.nvidia.com/content/precision-performance-floating-point-and-ieee-754-
compliance-nvidia-gpus includes more information on the floating point accuracy and
compliance of NVIDIA GPUs.
H.3.Compute Capability 3.x
H.3.1.Architecture
A multiprocessor consists of:
‣192 CUDA cores for arithmetic operations (see Arithmetic Instructions for
throughputs of arithmetic operations),
‣32 special function units for single-precision floating-point transcendental functions,
‣4 warp schedulers.
When a multiprocessor is given warps to execute, it first distributes them among
the four schedulers. Then, at every instruction issue time, each scheduler issues two
independent instructions for one of its assigned warps that is ready to execute, if any.
A multiprocessor has a read-only constant cache that is shared by all functional units
and speeds up reads from the constant memory space, which resides in device memory.
There is an L1 cache for each multiprocessor and an L2 cache shared by all
multiprocessors. The L1 cache is used to cache accesses to local memory, including
temporary register spills. The L2 cache is used to cache accesses to local and global
memory. The cache behavior (e.g., whether reads are cached in both L1 and L2 or in
L2 only) can be partially configured on a per-access basis using modifiers to the load
or store instruction. Some devices of compute capability 3.5 and devices of compute
capability 3.7 allow opt-in to caching of global memory in both L1 and L2 via compiler
options.
The same on-chip memory is used for both L1 and shared memory: It can be configured
as 48 KB of shared memory and 16 KB of L1 cache or as 16 KB of shared memory
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and 48 KB of L1 cache or as 32 KB of shared memory and 32 KB of L1 cache, using
cudaFuncSetCacheConfig()/cuFuncSetCacheConfig():
// Device code
__global__ void MyKernel()
{
...
}
// Host code
// Runtime API
// cudaFuncCachePreferShared: shared memory is 48 KB
// cudaFuncCachePreferEqual: shared memory is 32 KB
// cudaFuncCachePreferL1: shared memory is 16 KB
// cudaFuncCachePreferNone: no preference
cudaFuncSetCacheConfig(MyKernel, cudaFuncCachePreferShared)
The default cache configuration is "prefer none," meaning "no preference."
If a kernel is configured to have no preference, then it will default
to the preference of the current thread/context, which is set using
cudaDeviceSetCacheConfig()/cuCtxSetCacheConfig() (see the reference manual
for details). If the current thread/context also has no preference (which is again the
default setting), then whichever cache configuration was most recently used for any
kernel will be the one that is used, unless a different cache configuration is required to
launch the kernel (e.g., due to shared memory requirements). The initial configuration is
48 KB of shared memory and 16 KB of L1 cache.
Devices of compute capability 3.7 add an additional 64 KB of shared memory to each
of the above configurations, yielding 112 KB, 96 KB, and 80 KB shared memory per
multiprocessor, respectively. However, the maximum shared memory per thread block
remains 48 KB.
Applications may query the L2 cache size by checking the l2CacheSize device property
(see Device Enumeration). The maximum L2 cache size is 1.5 MB.
Each multiprocessor has a read-only data cache of 48 KB to speed up reads from device
memory. It accesses this cache either directly (for devices of compute capability 3.5
or 3.7), or via a texture unit that implements the various addressing modes and data
filtering mentioned in Texture and Surface Memory. When accessed via the texture unit,
the read-only data cache is also referred to as texture cache.
H.3.2.Global Memory
Global memory accesses for devices of compute capability 3.x are cached in L2 and for
devices of compute capability 3.5 or 3.7, may also be cached in the read-only data cache
described in the previous section; they are normally not cached in L1. Some devices of
compute capability 3.5 and devices of compute capability 3.7 allow opt-in to caching of
global memory accesses in L1 via the -Xptxas -dlcm=ca option to nvcc.
A cache line is 128 bytes and maps to a 128 byte aligned segment in device memory.
Memory accesses that are cached in both L1 and L2 are serviced with 128-byte memory
transactions whereas memory accesses that are cached in L2 only are serviced with
32-byte memory transactions. Caching in L2 only can therefore reduce over-fetch, for
example, in the case of scattered memory accesses.
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If the size of the words accessed by each thread is more than 4 bytes, a memory
request by a warp is first split into separate 128-byte memory requests that are issued
independently:
‣Two memory requests, one for each half-warp, if the size is 8 bytes,
‣Four memory requests, one for each quarter-warp, if the size is 16 bytes.
Each memory request is then broken down into cache line requests that are issued
independently. A cache line request is serviced at the throughput of L1 or L2 cache in
case of a cache hit, or at the throughput of device memory, otherwise.
Note that threads can access any words in any order, including the same words.
If a non-atomic instruction executed by a warp writes to the same location in global
memory for more than one of the threads of the warp, only one thread performs a write
and which thread does it is undefined.
Data that is read-only for the entire lifetime of the kernel can also be cached in the read-
only data cache described in the previous section by reading it using the __ldg()
function (see Read-Only Data Cache Load Function). When the compiler detects that
the read-only condition is satisfied for some data, it will use __ldg() to read it. The
compiler might not always be able to detect that the read-only condition is satisfied
for some data. Marking pointers used for loading such data with both the const and
__restrict__ qualifiers increases the likelihood that the compiler will detect the read-
only condition.
Figure 16 shows some examples of global memory accesses and corresponding memory
transactions.
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Figure16 Examples of Global Memory Accesses
Examples of Global Memory Accesses by a Warp, 4-Byte Word per Thread, and Associated
Memory Transactions for Compute Capabilities 3.x and Beyond
H.3.3.Shared Memory
Shared memory has 32 banks with two addressing modes that are described below.
The addressing mode can be queried using cudaDeviceGetSharedMemConfig() and
set using cudaDeviceSetSharedMemConfig() (see reference manual for more details).
Each bank has a bandwidth of 64 bits per clock cycle.
Figure 17 shows some examples of strided access.
Figure 18 shows some examples of memory read accesses that involve the broadcast
mechanism.
64-Bit Mode
Successive 64-bit words map to successive banks.
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A shared memory request for a warp does not generate a bank conflict between two
threads that access any sub-word within the same 64-bit word (even though the
addresses of the two sub-words fall in the same bank): In that case, for read accesses, the
64-bit word is broadcast to the requesting threads and for write accesses, each sub-word
is written by only one of the threads (which thread performs the write is undefined).
32-Bit Mode
Successive 32-bit words map to successive banks.
A shared memory request for a warp does not generate a bank conflict between two
threads that access any sub-word within the same 32-bit word or within two 32-bit
words whose indices i and j are in the same 64-word aligned segment (i.e., a segment
whose first index is a multiple of 64) and such that j=i+32 (even though the addresses of
the two sub-words fall in the same bank): In that case, for read accesses, the 32-bit words
are broadcast to the requesting threads and for write accesses, each sub-word is written
by only one of the threads (which thread performs the write is undefined).
H.4.Compute Capability 5.x
H.4.1.Architecture
A multiprocessor consists of:
‣128 CUDA cores for arithmetic operations (see Arithmetic Instructions for
throughputs of arithmetic operations),
‣32 special function units for single-precision floating-point transcendental functions,
‣4 warp schedulers.
When a multiprocessor is given warps to execute, it first distributes them among
the four schedulers. Then, at every instruction issue time, each scheduler issues one
instruction for one of its assigned warps that is ready to execute, if any.
A multiprocessor has:
‣a read-only constant cache that is shared by all functional units and speeds up reads
from the constant memory space, which resides in device memory,
‣a unified L1/texture cache of 24 KB used to cache reads from global memory,
‣64 KB of shared memory for devices of compute capability 5.0 or 96 KB of shared
memory for devices of compute capability 5.2.
The unified L1/texture cache is also used by the texture unit that implements the various
addressing modes and data filtering mentioned in Texture and Surface Memory.
There is also an L2 cache shared by all multiprocessors that is used to cache accesses to
local or global memory, including temporary register spills. Applications may query the
L2 cache size by checking the l2CacheSize device property (see Device Enumeration).
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The cache behavior (e.g., whether reads are cached in both the unified L1/texture cache
and L2 or in L2 only) can be partially configured on a per-access basis using modifiers to
the load instruction.
H.4.2.Global Memory
Global memory accesses are always cached in L2 and caching in L2 behaves in the same
way as for devices of compute capability 3.x (see Global Memory).
Data that is read-only for the entire lifetime of the kernel can also be cached in the
unified L1/texture cache described in the previous section by reading it using the
__ldg() function (see Read-Only Data Cache Load Function). When the compiler
detects that the read-only condition is satisfied for some data, it will use __ldg() to
read it. The compiler might not always be able to detect that the read-only condition
is satisfied for some data. Marking pointers used for loading such data with both the
const and __restrict__ qualifiers increases the likelihood that the compiler will
detect the read-only condition.
Data that is not read-only for the entire lifetime of the kernel cannot be cached in the
unified L1/texture cache for devices of compute capability 5.0. For devices of compute
capability 5.2, it is, by default, not cached in the unified L1/texture cache, but caching
may be enabled using the following mechanisms:
‣Perform the read using inline assembly with the appropriate modifier as described
in the PTX reference manual;
‣Compile with the -Xptxas -dlcm=ca compilation flag, in which case all reads are
cached, except reads that are performed using inline assembly with a modifier that
disables caching;
‣Compile with the -Xptxas -fscm=ca compilation flag, in which case all reads are
cached, including reads that are performed using inline assembly regardless of the
modifier used.
When caching is enabled using some the three mechanisms listed above, devices of
compute capability 5.2 will cache global memory reads in the unified L1/texture cache
for all kernel launches except for the kernel launches for which thread blocks consume
too much of the multiprocessor's resources. These exceptions are reported by the
profiler.
H.4.3.Shared Memory
Shared memory has 32 banks that are organized such that successive 32-bit words map
to successive banks. Each bank has a bandwidth of 32 bits per clock cycle.
A shared memory request for a warp does not generate a bank conflict between two
threads that access any address within the same 32-bit word (even though the two
addresses fall in the same bank): In that case, for read accesses, the word is broadcast to
the requesting threads and for write accesses, each address is written by only one of the
threads (which thread performs the write is undefined).
Figure 17 shows some examples of strided access.
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Figure 18 shows some examples of memory read accesses that involve the broadcast
mechanism.
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Linear addressing with a stride of one 32-bit word (no bank conflict).
Middle
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Figure17 Strided Shared Memory Accesses
Examples for devices of compute capability 3.x (in 32-bit mode) or compute capability 5.x
and 6.x
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Figure18 Irregular Shared Memory Accesses
Examples for devices of compute capability 3.x, 5.x, or 6.x.
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H.5.Compute Capability 6.x
H.5.1.Architecture
A multiprocessor consists of:
‣64 (compute capablity 6.0) or 128 (6.1 and 6.2) CUDA cores for arithmetic operations,
‣16 (6.0) or 32 (6.1 and 6.2) special function units for single-precision floating-point
transcendental functions,
‣2 (6.0) or 4 (6.1 and 6.2) warp schedulers.
When a multiprocessor is given warps to execute, it first distributes them among its
schedulers. Then, at every instruction issue time, each scheduler issues one instruction
for one of its assigned warps that is ready to execute, if any.
A multiprocessor has:
‣a read-only constant cache that is shared by all functional units and speeds up reads
from the constant memory space, which resides in device memory,
‣a unified L1/texture cache for reads from global memory of size 24 KB (6.0 and 6.2)
or 48 KB (6.1),
‣a shared memory of size 64 KB (6.0 and 6.2) or 96 KB (6.1).
The unified L1/texture cache is also used by the texture unit that implements the various
addressing modes and data filtering mentioned in Texture and Surface Memory.
There is also an L2 cache shared by all multiprocessors that is used to cache accesses to
local or global memory, including temporary register spills. Applications may query the
L2 cache size by checking the l2CacheSize device property (see Device Enumeration).
The cache behavior (e.g., whether reads are cached in both the unified L1/texture cache
and L2 or in L2 only) can be partially configured on a per-access basis using modifiers to
the load instruction.
H.5.2.Global Memory
Global memory behaves the same way as devices of compute capability 5.x (See Global
Memory).
H.5.3.Shared Memory
Shared memory behaves the same way as devices of compute capability 5.x (See Shared
Memory).
Compute Capabilities
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H.6.Compute Capability 7.x
H.6.1.Architecture
A multiprocessor consists of:
‣64 FP32 cores for single-precision arithmetic operations,
‣32 FP64 cores for double-precision arithmetic operations,
‣64 INT32 cores for integer math,
‣8 mixed-precision Tensor Cores for deep learning matrix arithmetic,
‣16 special function units for single-precision floating-point transcendental functions,
‣4 warp schedulers.
A multiprocessor statically distributes its warps among its schedulers. Then, at every
instruction issue time, each scheduler issues one instruction for one of its assigned warps
that is ready to execute, if any.
A multiprocessor has:
‣a read-only constant cache that is shared by all functional units and speeds up reads
from the constant memory space, which resides in device memory,
‣a combined data cache and shared memory with a total size of 128 KB.
Shared memory is partitioned out of the 128 KB data cache and can be configured to 0,
8, 16, 32, 64, or 96 KB. (See Shared Memory.) The remaining data cache serves as an L1
cache and is also used by the texture unit that implements the various addressing and
data filtering modes mentioned in Texture and Surface Memory.
H.6.2.Independent Thread Scheduling
The Volta architecture introduces Independent Thread Scheduling among threads in
a warp, enabling intra-warp synchronization patterns previously unavailable and
simplifying code changes when porting CPU code. However, this can lead to a rather
different set of threads participating in the executed code than intended if the developer
made assumptions about warp-synchronicity of previous hardware architectures.
Below are code patterns of concern and suggested corrective actions for Volta-safe code.
1. For applications using warp intrinsics (__shfl*, __any, __all, __ballot),
it is necessary that developers port their code to the new, safe, synchronizing
counterpart, with the *_sync suffix. The new warp intrinsics take in a mask of
threads that explicitly define which lanes (threads of a warp) must participate in the
warp intrinsic. See Warp Vote Functions and Warp Shuffle Functions for details.
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Since the intrinsics are available with CUDA 9.0+, (if necessary) code can be
executed conditionally with the following preprocessor macro:
#if defined(CUDART_VERSION) && CUDART_VERSION >= 9000
// *_sync intrinsic
#endif
These intrinsics are available on all architectures, not just Volta, and in most cases
a single code-base will suffice for all architectures. Note, however, that for Pascal
and earlier architectures, all threads in mask must execute the same warp intrinsic
instruction in convergence, and the union of all values in mask must be equal to the
warp's active mask. The following code pattern is valid on Volta, but not on Pascal
or earlier architectures.
if (tid % warpSize < 16) {
...
float swapped = __shfl_xor_sync(0xffffffff, val, 16);
...
} else {
...
float swapped = __shfl_xor_sync(0xffffffff, val, 16);
...
}
The replacement for __ballot(1) is __activemask(). Note that threads within
a warp can diverge even within a single code path. As a result, __activemask()
and __ballot(1) may return only a subset of the threads on the current code
path. The following invalid code example sets bit i of output to 1 when data[i]
is greater than threshold. __activemask() is used in an attempt to enable cases
where dataLen is not a multiple of 32.
// Sets bit in output[] to 1 if the correspond element in data[i]
// is greater than ‘threshold’, using 32 threads in a warp.
for(int i=warpLane; i<dataLen; i+=warpSize) {
unsigned active = __activemask();
unsigned bitPack = __ballot_sync(active, data[i] > threshold);
if (warpLane == 0)
output[i/32] = bitPack;
}
This code is invalid because CUDA does not guarantee that the warp will diverge
ONLY at the loop condition. When divergence happens for other reasons, conflicting
results will be computed for the same 32-bit output element by different subsets
of threads in the warp. A correct code might use a non-divergent loop condition
together with __ballot_sync() to safely enumerate the set of threads in the warp
participating in the threshold calculation as follows.
for(int i=warpLane; i-warpLane<dataLen; i+=warpSize) {
unsigned active = __ballot_sync(0xFFFFFFFF, i < dataLen);
if (i < dataLen) {
unsigned bitPack = __ballot_sync(active, data[i] > threshold);
if (warpLane == 0)
output[i/32] = bitPack;
}
}
Discovery Pattern demonstrates a valid usecase for __activemask().
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2. If applications have warp-synchronous codes, they will need to insert the new
__syncwarp() warp-wide barrier synchronization instruction between any
steps where data is exchanged between threads via global or shared memory.
Assumptions that code is executed in lockstep or that reads/writes from separate
threads are visible across a warp without synchronization are invalid.
float s_buff[tid] = val;
__syncthreads();
// Inter-warp reduction
for (int i=BSIZE/2; i>32; i/=2){
s_buff[tid] += s_buff[tid+i];
__syncthreads();
}
// Intra-warp reduction
// Butterfly reduction simplifies syncwarp mask
if (tid < 32) {
float temp;
temp = s_buff[tid^16]; __syncwarp();
s_buff[tid] += temp; __syncwarp();
temp = s_buff[tid^ 8]; __syncwarp();
s_buff[tid] += temp; __syncwarp();
temp = s_buff[tid^ 4]; __syncwarp();
s_buff[tid] += temp; __syncwarp();
temp = s_buff[tid^ 2]; __syncwarp();
s_buff[tid] += temp; __syncwarp();
}
if (tid == 0) {
*output = s_buff[0] + s_buff[1];
}
__syncthreads();
3. Although __syncthreads() has been consistently documented as synchronizing
all threads in the thread block, Pascal and prior architectures could only enforce
synchronization at the warp level. In certain cases, this allowed a barrier to succeed
without being executed by every thread as long as at least some thread in every
warp reached the barrier. Starting with Volta, the CUDA built-in __syncthreads()
and PTX instruction bar.sync (and their derivatives) are enforced per thread and
thus will not succeed until reached by all non-exited threads in the block. Code
exploiting the previous behavior will likely deadlock and must be modified to
ensure that all non-exited threads reach the barrier.
The racecheck and synccheck tools provided by cuda-memcheck can aid in locating
violations of points 2 and 3.
To aid migration while implementing the above-mentioned corrective actions,
developers can opt-in to the Pascal scheduling model that does not support independent
thread scheduling. See Application Compatibility for details.
H.6.3.Global Memory
Global memory behaves the same way as devices of compute capability 5.x (See Global
Memory).
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H.6.4.Shared Memory
The ratio of the 128 KB data cache reserved for shared memory is referred to as
the shared memory carveout. The carveout can be configured on a per kernel basis.
Supported shared memory capacities are 0, 8, 16, 32, 64, or 96 KB.
Rather than extending the existing cudaFuncSetCacheConfig() API used for the
Kepler architecture to support an extended set of shared capacities, a new runtime
API has been designed. The new API addresses two key deficiencies in the old API.
First, defining separate enums for each capacity ratio does not scale elegantly to many
options. Second, because the legacy API treated shared memory capacities as hard
requirements for kernel launch, interleaving kernels with different shared memory
requests would needlessly serialize launches behind shared memory reconfigurations.
The new API uses the function cudaFuncSetAttribute() as follows.
// Device code
__global__ void MyKernel(...)
{
...
}
// Host code
int carveout = 50; // 50%
// Named Carevout Values:
// carveout = cudaSharedmemCarveoutDefault; // (-1)
// carveout = cudaSharedmemCarveoutMaxL1; // (0)
// carveout = cudaSharedmemCarveoutMaxShared; // (100)
cudaFuncSetAttribute(MyKernel, cudaFuncAttributePreferredSharedMemoryCarveout,
carveout);
MyKernel <<<gridDim, blockDim>>>(...);
Here the integer carveout specifies the shared memory carveout preference in percent
of the total resource. This is only a hint, and the driver can choose a different ratio if
required to execute the function or to avoid thrashing.
Compute capability 7.0 devices allow a single thread block to address the full 96 KB of
shared memory. Kernels relying on shared memory allocations over 48 KB per block
are architecture-specific, as such they must use dynamic shared memory (rather than
statically sized arrays) and require an explicit opt-in using cudaFuncSetAttribute()
as follows.
// Device code
__global__ void MyKernel(...)
{
...
}
// Host code
int maxbytes = 98304; // 96 KB
cudaFuncSetAttribute(MyKernel, cudaFuncAttributeMaxDynamicSharedMemorySize,
maxbytes);
MyKernel <<<gridDim, blockDim>>>(...);
Otherwise, shared memory behaves the same way as devices of compute capability 5.x
(See Shared Memory).
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AppendixI.
DRIVER API
This appendix assumes knowledge of the concepts described in CUDA C Runtime.
The driver API is implemented in the cuda dynamic library (cuda.dll or cuda.so)
which is copied on the system during the installation of the device driver. All its entry
points are prefixed with cu.
It is a handle-based, imperative API: Most objects are referenced by opaque handles that
may be specified to functions to manipulate the objects.
The objects available in the driver API are summarized in Table 15.
Table15 Objects Available in the CUDA Driver API
Object Handle Description
Device CUdevice CUDA-enabled device
Context CUcontext Roughly equivalent to a CPU process
Module CUmodule Roughly equivalent to a dynamic library
Function CUfunction Kernel
Heap memory CUdeviceptr Pointer to device memory
CUDA array CUarray Opaque container for one-dimensional or two-
dimensional data on the device, readable via
texture or surface references
Texture reference CUtexref Object that describes how to interpret texture
memory data
Surface reference CUsurfref Object that describes how to read or write CUDA
arrays
Event CUevent Object that describes a CUDA event
The driver API must be initialized with cuInit() before any function from the driver
API is called. A CUDA context must then be created that is attached to a specific device
and made current to the calling host thread as detailed in Context.
Within a CUDA context, kernels are explicitly loaded as PTX or binary objects by the
host code as described in Module. Kernels written in C must therefore be compiled
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separately into PTX or binary objects. Kernels are launched using API entry points as
described in Kernel Execution.
Any application that wants to run on future device architectures must load PTX,
not binary code. This is because binary code is architecture-specific and therefore
incompatible with future architectures, whereas PTX code is compiled to binary code at
load time by the device driver.
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Here is the host code of the sample from Kernels written using the driver API:
int main()
{
int N = ...;
size_t size = N * sizeof(float);
// Allocate input vectors h_A and h_B in host memory
float* h_A = (float*)malloc(size);
float* h_B = (float*)malloc(size);
// Initialize input vectors
...
// Initialize
cuInit(0);
// Get number of devices supporting CUDA
int deviceCount = 0;
cuDeviceGetCount(&deviceCount);
if (deviceCount == 0) {
printf("There is no device supporting CUDA.\n");
exit (0);
}
// Get handle for device 0
CUdevice cuDevice;
cuDeviceGet(&cuDevice, 0);
// Create context
CUcontext cuContext;
cuCtxCreate(&cuContext, 0, cuDevice);
// Create module from binary file
CUmodule cuModule;
cuModuleLoad(&cuModule, "VecAdd.ptx");
// Allocate vectors in device memory
CUdeviceptr d_A;
cuMemAlloc(&d_A, size);
CUdeviceptr d_B;
cuMemAlloc(&d_B, size);
CUdeviceptr d_C;
cuMemAlloc(&d_C, size);
// Copy vectors from host memory to device memory
cuMemcpyHtoD(d_A, h_A, size);
cuMemcpyHtoD(d_B, h_B, size);
// Get function handle from module
CUfunction vecAdd;
cuModuleGetFunction(&vecAdd, cuModule, "VecAdd");
// Invoke kernel
int threadsPerBlock = 256;
int blocksPerGrid =
(N + threadsPerBlock - 1) / threadsPerBlock;
void* args[] = { &d_A, &d_B, &d_C, &N };
cuLaunchKernel(vecAdd,
blocksPerGrid, 1, 1, threadsPerBlock, 1, 1,
0, 0, args, 0);
...
}
Full code can be found in the vectorAddDrv CUDA sample.
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I.1.Context
A CUDA context is analogous to a CPU process. All resources and actions performed
within the driver API are encapsulated inside a CUDA context, and the system
automatically cleans up these resources when the context is destroyed. Besides objects
such as modules and texture or surface references, each context has its own distinct
address space. As a result, CUdeviceptr values from different contexts reference
different memory locations.
A host thread may have only one device context current at a time. When a context
is created with cuCtxCreate(), it is made current to the calling host thread. CUDA
functions that operate in a context (most functions that do not involve device
enumeration or context management) will return CUDA_ERROR_INVALID_CONTEXT if a
valid context is not current to the thread.
Each host thread has a stack of current contexts. cuCtxCreate() pushes the new
context onto the top of the stack. cuCtxPopCurrent() may be called to detach the
context from the host thread. The context is then "floating" and may be pushed as the
current context for any host thread. cuCtxPopCurrent() also restores the previous
current context, if any.
A usage count is also maintained for each context. cuCtxCreate() creates a
context with a usage count of 1. cuCtxAttach() increments the usage count and
cuCtxDetach() decrements it. A context is destroyed when the usage count goes to 0
when calling cuCtxDetach() or cuCtxDestroy().
Usage count facilitates interoperability between third party authored code operating in
the same context. For example, if three libraries are loaded to use the same context, each
library would call cuCtxAttach() to increment the usage count and cuCtxDetach()
to decrement the usage count when the library is done using the context. For most
libraries, it is expected that the application will have created a context before loading
or initializing the library; that way, the application can create the context using its
own heuristics, and the library simply operates on the context handed to it. Libraries
that wish to create their own contexts - unbeknownst to their API clients who may or
may not have created contexts of their own - would use cuCtxPushCurrent() and
cuCtxPopCurrent() as illustrated in Figure 19.
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Library Initialization Call
cuCtxCreate()
Initialize
context
cuCtxPopCurrent()
Library Call
cuCtxPushCurrent()
Use
context
cuCtxPopCurrent()
Figure19 Library Context Management
I.2.Module
Modules are dynamically loadable packages of device code and data, akin to DLLs in
Windows, that are output by nvcc (see Compilation with NVCC). The names for all
symbols, including functions, global variables, and texture or surface references, are
maintained at module scope so that modules written by independent third parties may
interoperate in the same CUDA context.
This code sample loads a module and retrieves a handle to some kernel:
CUmodule cuModule;
cuModuleLoad(&cuModule, "myModule.ptx");
CUfunction myKernel;
cuModuleGetFunction(&myKernel, cuModule, "MyKernel");
This code sample compiles and loads a new module from PTX code and parses
compilation errors:
#define BUFFER_SIZE 8192
CUmodule cuModule;
CUjit_option options[3];
void* values[3];
char* PTXCode = "some PTX code";
char error_log[BUFFER_SIZE];
int err;
options[0] = CU_JIT_ERROR_LOG_BUFFER;
values[0] = (void*)error_log;
options[1] = CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES;
values[1] = (void*)BUFFER_SIZE;
options[2] = CU_JIT_TARGET_FROM_CUCONTEXT;
values[2] = 0;
err = cuModuleLoadDataEx(&cuModule, PTXCode, 3, options, values);
if (err != CUDA_SUCCESS)
printf("Link error:\n%s\n", error_log);
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This code sample compiles, links, and loads a new module from multiple PTX codes and
parses link and compilation errors:
#define BUFFER_SIZE 8192
CUmodule cuModule;
CUjit_option options[6];
void* values[6];
float walltime;
char error_log[BUFFER_SIZE], info_log[BUFFER_SIZE];
char* PTXCode0 = "some PTX code";
char* PTXCode1 = "some other PTX code";
CUlinkState linkState;
int err;
void* cubin;
size_t cubinSize;
options[0] = CU_JIT_WALL_TIME;
values[0] = (void*)&walltime;
options[1] = CU_JIT_INFO_LOG_BUFFER;
values[1] = (void*)info_log;
options[2] = CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES;
values[2] = (void*)BUFFER_SIZE;
options[3] = CU_JIT_ERROR_LOG_BUFFER;
values[3] = (void*)error_log;
options[4] = CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES;
values[4] = (void*)BUFFER_SIZE;
options[5] = CU_JIT_LOG_VERBOSE;
values[5] = (void*)1;
cuLinkCreate(6, options, values, &linkState);
err = cuLinkAddData(linkState, CU_JIT_INPUT_PTX,
(void*)PTXCode0, strlen(PTXCode0) + 1, 0, 0, 0, 0);
if (err != CUDA_SUCCESS)
printf("Link error:\n%s\n", error_log);
err = cuLinkAddData(linkState, CU_JIT_INPUT_PTX,
(void*)PTXCode1, strlen(PTXCode1) + 1, 0, 0, 0, 0);
if (err != CUDA_SUCCESS)
printf("Link error:\n%s\n", error_log);
cuLinkComplete(linkState, &cubin, &cubinSize);
printf("Link completed in %fms. Linker Output:\n%s\n", walltime, info_log);
cuModuleLoadData(cuModule, cubin);
cuLinkDestroy(linkState);
Full code can be found in the ptxjit CUDA sample.
I.3.Kernel Execution
cuLaunchKernel() launches a kernel with a given execution configuration.
Parameters are passed either as an array of pointers (next to last parameter of
cuLaunchKernel()) where the nth pointer corresponds to the nth parameter and
points to a region of memory from which the parameter is copied, or as one of the extra
options (last parameter of cuLaunchKernel()).
When parameters are passed as an extra option (the
CU_LAUNCH_PARAM_BUFFER_POINTER option), they are passed as a pointer to a single
buffer where parameters are assumed to be properly offset with respect to each other by
matching the alignment requirement for each parameter type in device code.
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Alignment requirements in device code for the built-in vector types are listed in Table
3. For all other basic types, the alignment requirement in device code matches the
alignment requirement in host code and can therefore be obtained using __alignof().
The only exception is when the host compiler aligns double and long long (and
long on a 64-bit system) on a one-word boundary instead of a two-word boundary (for
example, using gcc's compilation flag -mno-align-double) since in device code these
types are always aligned on a two-word boundary.
CUdeviceptr is an integer, but represents a pointer, so its alignment requirement is
__alignof(void*).
The following code sample uses a macro (ALIGN_UP()) to adjust the offset
of each parameter to meet its alignment requirement and another macro
(ADD_TO_PARAM_BUFFER()) to add each parameter to the parameter buffer passed to
the CU_LAUNCH_PARAM_BUFFER_POINTER option.
#define ALIGN_UP(offset, alignment) \
(offset) = ((offset) + (alignment) - 1) & ~((alignment) - 1)
char paramBuffer[1024];
size_t paramBufferSize = 0;
#define ADD_TO_PARAM_BUFFER(value, alignment) \
do { \
paramBufferSize = ALIGN_UP(paramBufferSize, alignment); \
memcpy(paramBuffer + paramBufferSize, \
&(value), sizeof(value)); \
paramBufferSize += sizeof(value); \
} while (0)
int i;
ADD_TO_PARAM_BUFFER(i, __alignof(i));
float4 f4;
ADD_TO_PARAM_BUFFER(f4, 16); // float4's alignment is 16
char c;
ADD_TO_PARAM_BUFFER(c, __alignof(c));
float f;
ADD_TO_PARAM_BUFFER(f, __alignof(f));
CUdeviceptr devPtr;
ADD_TO_PARAM_BUFFER(devPtr, __alignof(devPtr));
float2 f2;
ADD_TO_PARAM_BUFFER(f2, 8); // float2's alignment is 8
void* extra[] = {
CU_LAUNCH_PARAM_BUFFER_POINTER, paramBuffer,
CU_LAUNCH_PARAM_BUFFER_SIZE, &paramBufferSize,
CU_LAUNCH_PARAM_END
};
cuLaunchKernel(cuFunction,
blockWidth, blockHeight, blockDepth,
gridWidth, gridHeight, gridDepth,
0, 0, 0, extra);
The alignment requirement of a structure is equal to the maximum of the alignment
requirements of its fields. The alignment requirement of a structure that contains built-
in vector types, CUdeviceptr, or non-aligned double and long long, might therefore
differ between device code and host code. Such a structure might also be padded
differently. The following structure, for example, is not padded at all in host code, but it
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is padded in device code with 12 bytes after field f since the alignment requirement for
field f4 is 16.
typedef struct {
float f;
float4 f4;
} myStruct;
I.4.Interoperability between Runtime and Driver
APIs
An application can mix runtime API code with driver API code.
If a context is created and made current via the driver API, subsequent runtime calls will
pick up this context instead of creating a new one.
If the runtime is initialized (implicitly as mentioned in CUDA C Runtime),
cuCtxGetCurrent() can be used to retrieve the context created during initialization.
This context can be used by subsequent driver API calls.
Device memory can be allocated and freed using either API. CUdeviceptr can be cast to
regular pointers and vice-versa:
CUdeviceptr devPtr;
float* d_data;
// Allocation using driver API
cuMemAlloc(&devPtr, size);
d_data = (float*)devPtr;
// Allocation using runtime API
cudaMalloc(&d_data, size);
devPtr = (CUdeviceptr)d_data;
In particular, this means that applications written using the driver API can invoke
libraries written using the runtime API (such as cuFFT, cuBLAS, ...).
All functions from the device and version management sections of the reference manual
can be used interchangeably.
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AppendixJ.
CUDA ENVIRONMENT VARIABLES
Environment variables related to the Multi-Process Service are documented in the Multi-
Process Service section of the GPU Deployment and Management guide.
Table16 CUDA Environment Variables
Category Variable Values Description
CUDA_VISIBLE_DEVICES A comma-
separated
sequence of
GPU identifiers
GPU identifiers are given as integer
indices or as UUID strings. GPU
UUID strings should follow the same
format as given by nvidia-smi,
such as GPU-8932f937-d72c-4106-
c12f-20bd9faed9f6. However, for
convenience, abbreviated forms
are allowed; simply specify enough
digits from the beginning of the GPU
UUID to uniquely identify that GPU
in the target system. For example,
CUDA_VISIBLE_DEVICES=GPU-8932f937
may be a valid way to refer to the above
GPU UUID, assuming no other GPU in the
system shares this prefix.
Only the devices whose index is present
in the sequence are visible to CUDA
applications and they are enumerated
in the order of the sequence. If one of
the indices is invalid, only the devices
whose index precedes the invalid index
are visible to CUDA applications. For
example, setting CUDA_VISIBLE_DEVICES
to 2,1 causes device 0 to be invisible
and device 2 to be enumerated before
device 1. Setting CUDA_VISIBLE_DEVICES
to 0,2,-1,1 causes devices 0 and 2 to be
visible and device 1 to be invisible.
CUDA_MANAGED_
FORCE_DEVICE_ALLOC
0 or 1 (default
is 0)
Forces the driver to place all managed
allocations in device memory.
Device
Enumeration
and Properties
CUDA_DEVICE_ORDER FASTEST_FIRST,
PCI_BUS_ID,
FASTEST_FIRST causes CUDA to guess
which device is fastest using a simple
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Category Variable Values Description
(default is
FASTEST_FIRST)
heuristic, and make that device 0, leaving
the order of the rest of the devices
unspecified. PCI_BUS_ID orders devices by
PCI bus ID in ascending order.
CUDA_CACHE_DISABLE 0 or 1 (default
is 0)
Disables caching (when set to 1) or
enables caching (when set to 0) for just-
in-time-compilation. When disabled, no
binary code is added to or retrieved from
the cache.
CUDA_CACHE_PATH filepath Specifies the folder where the just-in-
time compiler caches binary codes; the
default values are:
‣on Windows, %APPDATA%\NVIDIA
\ComputeCache,
‣on MacOS, $HOME/Library/
Application\ Support/NVIDIA/
ComputeCache,
‣on Linux, ~/.nv/ComputeCache
CUDA_CACHE_MAXSIZE integer (default
is 33554432
(32 MB) and
maximum is
4294967296 (4
GB))
Specifies the size in bytes of the cache
used by the just-in-time compiler. Binary
codes whose size exceeds the cache size
are not cached. Older binary codes are
evicted from the cache to make room for
newer binary codes if needed.
Compilation
CUDA_FORCE_PTX_JIT 0 or 1 (default
is 0)
When set to 1, forces the device driver
to ignore any binary code embedded
in an application (see Application
Compatibility) and to just-in-time compile
embedded PTX code instead. If a kernel
does not have embedded PTX code,
it will fail to load. This environment
variable can be used to validate that
PTX code is embedded in an application
and that its just-in-time compilation
works as expected to guarantee
application forward compatibility with
future architectures (see Just-in-Time
Compilation).
CUDA_LAUNCH_
BLOCKING
0 or 1 (default
is 0)
Disables (when set to 1) or enables (when
set to 0) asynchronous kernel launches.
CUDA_DEVICE_MAX_
CONNECTIONS
1 to 32 (default
is 8)
Sets the number of compute and copy
engine concurrent connections (work
queues) from the host to each device of
compute capability 3.5 and above.
Execution
CUDA_AUTO_BOOST 0 or 1 Overrides the autoboost behavior set
by the --auto-boost-default option of
nvidia-smi. If an application requests
via this environment variable a behavior
that is different from nvidia-smi's, its
request is honored if there is no other
application currently running on the
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Category Variable Values Description
same GPU that successfully requested a
different behavior, otherwise it is ignored.
CUDA_ENABLE_CRC_
CHECK
0 or 1 (default
is 0)
When set to 1, commands and data sent
to the GPU are error-checked to ensure
they are not corrupted.
cuda-gdb (on
Mac and Linux
platforms)
CUDA_DEVICE_WAITS_
ON_EXCEPTION
0 or 1 (default
is 0)
When set to 1, a CUDA application will
halt when a device exception occurs,
allowing a debugger to be attached for
further debugging.
CUDA_DEVICE Integer (default
is 0)
Specifies the index of the device to
profile.
COMPUTE_PROFILE 0 or 1 (default
is 0)
Disables profiling (when set to 0) or
enables profiling (when set to 1).
COMPUTE_PROFILE_
CONFIG
Path Specifies the configuration file to set
profiling options and select performance
counters.
COMPUTE_PROFILE_LOG Path Specifies the file used to save
the profiling output. In case of
multiple contexts, use '%d' in the
COMPUTE_PROFILE_LOG to generate
separate output files for each context
- with '%d' substituted by the context
number.
Driver-Based
Profiler (these
variables have
no impact
on the Visual
Profiler or the
command line
profiler nvprof)
COMPUTE_PROFILE_CSV 0 or 1 (default
is 0)
When set to 1, the output will be in
comma-separated format.
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AppendixK.
UNIFIED MEMORY PROGRAMMING
K.1.Unified Memory Introduction
Unified Memory is a component of the CUDA programming model, first introduced
in CUDA 6.0, that defines a managed memory space in which all processors see a single
coherent memory image with a common address space.
A processor refers to any independent execution unit with a dedicated MMU. This
includes both CPUs and GPUs of any type and architecture.
The underlying system manages data access and locality within a CUDA program
without need for explicit memory copy calls. This benefits GPU programming in two
primary ways:
‣GPU programming is simplified by unifying memory spaces coherently across all
GPUs and CPUs in the system and by providing tighter and more straightforward
language integration for CUDA programmers.
‣Data access speed is maximized by transparently migrating data towards the
processor using it.
In simple terms, Unified Memory eliminates the need for explicit data movement via the
cudaMemcpy*() routines without the performance penalty incurred by placing all data
into zero-copy memory. Data movement, of course, still takes place, so a program’s run
time typically does not decrease; Unified Memory instead enables the writing of simpler
and more maintainable code.
Unified Memory offers a “single-pointer-to-data” model that is conceptually similar to
CUDA’s zero-copy memory. One key difference between the two is that with zero-copy
allocations the physical location of memory is pinned in CPU system memory such that
a program may have fast or slow access to it depending on where it is being accessed
from. Unified Memory, on the other hand, decouples memory and execution spaces so
that all data accesses are fast.
The term Unified Memory describes a system that provides memory management
services to a wide range of programs, from those targeting the Runtime API down to
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those using the Virtual ISA (PTX). Part of this system defines the managed memory
space that opts in to Unified Memory services.
Managed memory is interoperable and interchangeable with device-specific allocations,
such as those created using the cudaMalloc() routine. All CUDA operations that are
valid on device memory are also valid on managed memory; the primary difference is
that the host portion of a program is able to reference and access the memory as well.
K.1.1.System Requirements
Unified Memory has two basic requirements:
‣a GPU with SM architecture 3.0 or higher (Kepler class or newer)
‣a 64-bit host application and non-embedded operating system (Linux, Windows,
macOS)
GPUs with SM architecture 6.x or higher (Pascal class or newer) provide additional
Unified Memory features such as on-demand page migration and GPU memory
oversubscription that are outlined throughout this document. Note that currently
these features are only supported on Linux operating systems. Applications running
on Windows (whether in TCC or WDDM mode) or macOS will use the basic Unified
Memory model as on pre-6.x architectures even when they are running on hardware
with compute capability 6.x or higher. See Data Migration and Coherency for details.
K.1.2.Simplifying GPU Programming
Unification of memory spaces means that there is no longer any need for explicit
memory transfers between host and device. Any allocation created in the managed
memory space is automatically migrated to where it is needed.
A program allocates managed memory in one of two ways: via the
cudaMallocManaged() routine, which is semantically similar to cudaMalloc(); or by
defining a global __managed__ variable, which is semantically similar to a __device__
variable. Precise definitions of these are found later in this document.
On supporting platforms with devices of compute capability 6.x, Unified Memory will
enable applications to allocate and share data using the default system allocator. This
allows the GPU to access the entire system virtual memory without using a special
allocator.
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The following code examples illustrate how the use of managed memory can change the
way in which host code is written. First, a simple program written without the benefit of
unified memory:
__global__ void AplusB( int *ret, int a, int b) {
ret[threadIdx.x] = a + b + threadIdx.x;
}
int main() {
int *ret;
cudaMalloc(&ret, 1000 * sizeof(int));
AplusB<<< 1, 1000 >>>(ret, 10, 100);
int *host_ret = (int *)malloc(1000 * sizeof(int));
cudaMemcpy(host_ret, ret, 1000 * sizeof(int), cudaMemcpyDefault);
for(int i=0; i<1000; i++)
printf("%d: A+B = %d\n", i, host_ret[i]);
free(host_ret);
cudaFree(ret);
return 0;
}
This first example combines two numbers together on the GPU with a per-thread ID and
returns the values in an array. Without managed memory, both host- and device-side
storage for the return values is required (host_ret and ret in the example), as is an
explicit copy between the two using cudaMemcpy().
Compare this with the Unified Memory version of the program, which allows direct
access of GPU data from the host. Notice the cudaMallocManaged() routine, which
returns a pointer valid from both host and device code. This allows ret to be used
without a separate host_ret copy, greatly simplifying and reducing the size of the
program.
__global__ void AplusB(int *ret, int a, int b) {
ret[threadIdx.x] = a + b + threadIdx.x;
}
int main() {
int *ret;
cudaMallocManaged(&ret, 1000 * sizeof(int));
AplusB<<< 1, 1000 >>>(ret, 10, 100);
cudaDeviceSynchronize();
for(int i=0; i<1000; i++)
printf("%d: A+B = %d\n", i, ret[i]);
cudaFree(ret);
return 0;
}
Finally, language integration allows direct reference of a GPU-declared __managed__
variable and simplifies a program further when global variables are used.
__device__ __managed__ int ret[1000];
__global__ void AplusB(int a, int b) {
ret[threadIdx.x] = a + b + threadIdx.x;
}
int main() {
AplusB<<< 1, 1000 >>>(10, 100);
cudaDeviceSynchronize();
for(int i=0; i<1000; i++)
printf("%d: A+B = %d\n", i, ret[i]);
return 0;
}
Note the absence of explicit cudaMemcpy() commands and the fact that the return array
ret is visible on both CPU and GPU.
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It is worth a comment on the synchronization between host and device. Notice how in
the non-managed example, the synchronous cudaMemcpy() routine is used both to
synchronize the kernel (that is, to wait for it to finish running), and to transfer the data
to the host. The Unified Memory examples do not call cudaMemcpy() and so require an
explicit cudaDeviceSynchronize() before the host program can safely use the output
from the GPU.
An alternative here would be to set the environment variable
CUDA_LAUNCH_BLOCKING=1, ensuring that all kernel launches complete
synchronously. This simplifies the code by eliminating all explicit synchronization, but
obviously has broader impact on execution behavior as a whole.
K.1.3.Data Migration and Coherency
Unified Memory attempts to optimize memory performance by migrating data towards
the device where it is being accessed (that is, moving data to host memory if the CPU
is accessing it and to device memory if the GPU will access it). Data migration is
fundamental to Unified Memory, but is transparent to a program. The system will try
to place data in the location where it can most efficiently be accessed without violating
coherency.
The physical location of data is invisible to a program and may be changed at any
time, but accesses to the data’s virtual address will remain valid and coherent from
any processor regardless of locality. Note that maintaining coherence is the primary
requirement, ahead of performance; within the constraints of the host operating system,
the system is permitted to either fail accesses or move data in order to maintain global
coherence between processors.
GPU architectures of compute capability lower than 6.x do not support fine-grained
movement of the managed data to GPU on-demand. Whenever a GPU kernel is
launched all managed memory generally has to be transfered to GPU memory to avoid
faulting on memory access. With compute capability 6.x a new GPU page faulting
mechanism is introduced that provides more seamless Unified Memory functionality.
Combined with the system-wide virtual address space, page faulting provides several
benefits. First, page faulting means that the CUDA system software doesn’t need to
synchronize all managed memory allocations to the GPU before each kernel launch.
If a kernel running on the GPU accesses a page that is not resident in its memory, it
faults, allowing the page to be automatically migrated to the GPU memory on-demand.
Alternatively, the page may be mapped into the GPU address space for access over
the PCIe or NVLink interconnects (mapping on access can sometimes be faster than
migration). Note that Unified Memory is system-wide: GPUs (and CPUs) can fault on
and migrate memory pages either from CPU memory or from the memory of other
GPUs in the system.
K.1.4.GPU Memory Oversubscription
Devices of compute capability lower than 6.x cannot allocate more managed memory
than the physical size of GPU memory.
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Devices of compute capability 6.x extend addressing mode to support 49-bit virtual
addressing. This is large enough to cover the 48-bit virtual address spaces of modern
CPUs, as well as the GPU’s own memory. The large virtual address space and page
faulting capability enable applications to access the entire system virtual memory, not
limited by the physical memory size of any one processor. This means that applications
can oversubscribe the memory system: in other words they can allocate, access, and
share arrays larger than the total physical capacity of the system, enabling out-of-core
processing of very large datasets. cudaMallocManaged will not run out of memory as
long as there is enough system memory available for the allocation.
K.1.5.Multi-GPU Support
For devices of compute capability lower than 6.x managed memory allocation behaves
identically to unmanaged memory allocated using cudaMalloc(): the current active
device is the home for the physical allocation, and all other GPUs receive peer mappings
to the memory. This means that other GPUs in the system will access the memory at
reduced bandwidth over the PCIe bus. Note that if peer mappings are not supported
between the GPUs in the system, then the managed memory pages are placed in CPU
system memory (“zero-copy” memory), and all GPUs will experience PCIe bandwidth
restrictions. See Managed Memory with Multi-GPU Programs on pre-6.x Architectures
for details.
Managed allocations on systems with devices of compute capability 6.x are visible to all
GPUs and can migrate to any processor on-demand.
K.2.Programming Model
K.2.1.Managed Memory Opt In
Most platforms require a program to opt in to automatic data management by either
annotating a __device__ variable with the __managed__ keyword (see the
Language Integration section) or by using a new cudaMallocManaged() call to allocate
data.
Devices of compute capability lower than 6.x must always allocate managed memory on
the heap, either with an allocator or by declaring global storage. It is not possible either
to associate previously allocated memory with Unified Memory, or to have the Unified
Memory system manage a CPU or a GPU stack pointer.
Starting with CUDA 8.0 and on supporting systems with devices of compute capability
6.x, memory allocated with the default OS allocator (e.g. malloc or new) can be accessed
from both GPU code and CPU code using the same pointer. On these systems, Unified
Memory is the default: there is no need to use a special allocator or the creation of a
specially managed memory pool.
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K.2.1.1.Explicit Allocation Using cudaMallocManaged()
Unified memory is most commonly created using an allocation function that is
semantically and syntactically similar to the standard CUDA allocator, cudaMalloc().
The function description is as follows:
cudaError_t cudaMallocManaged(void **devPtr,
size_t size,
unsigned int flags=0);
The cudaMallocManaged() function allocates size bytes of managed memory on the
GPU and returns a pointer in devPtr. The pointer is valid on all GPUs and the CPU in
the system, although program accesses to this pointer must obey the concurrency rules
of the Unified Memory programming model (see Coherency and Concurrency). Below is
a simple example, showing the use of cudaMallocManaged():
__global__ void printme(char *str) {
printf(str);
}
int main() {
// Allocate 100 bytes of memory, accessible to both Host and Device code
char *s;
cudaMallocManaged(&s, 100);
// Note direct Host-code use of "s"
strncpy(s, "Hello Unified Memory\n", 99);
// Here we pass "s" to a kernel without explicitly copying
printme<<< 1, 1 >>>(s);
cudaDeviceSynchronize();
// Free as for normal CUDA allocations
cudaFree(s);
return 0;
}
A program’s behavior is functionally unchanged when cudaMalloc() is replaced
with cudaMallocManaged(); however, the program should go on to eliminate explicit
memory copies and take advantage of automatic migration. Additionally, dual pointers
(one to host and one to device memory) can be eliminated.
Device code is not able to call cudaMallocManaged(). All managed memory must be
allocated from the host or at global scope (see the next section). Allocations on the device
heap using malloc() in a kernel will not be created in the managed memory space, and
so will not be accessible to CPU code.
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K.2.1.2.Global-Scope Managed Variables Using __managed__
File-scope and global-scope CUDA __device__ variables may also opt-in to Unified
Memory management by adding a new __managed__ annotation to the declaration.
These may then be referenced directly from either host or device code, as follows:
__device__ __managed__ int x[2];
__device__ __managed__ int y;
__global__ void kernel() {
x[1] = x[0] + y;
}
int main() {
x[0] = 3;
y = 5;
kernel<<< 1, 1 >>>();
cudaDeviceSynchronize();
printf("result = %d\n", x[1]);
return 0;
}
All semantics of the original __device__ memory space, along with some additional
unified-memory-specific constraints, are inherited by the managed variable. See
Compilation with NVCC) in the CUDA C Programming Guide for details.
Note that variables marked __constant__ may not also be marked as __managed__;
this annotation is reserved for __device__ variables only. Constant memory must be set
either statically at compile time or by using cudaMemcpyToSymbol() as usual in CUDA.
K.2.2.Coherency and Concurrency
Simultaneous access to managed memory on devices of compute capability lower than
6.x is not possible, because coherence could not be guaranteed if the CPU accessed
a Unified Memory allocation while a GPU kernel was active. However, devices of
compute capability 6.x on supporting operating systems allow the CPUs and GPUs
to access Unified Memory allocations simultaneously via the new page faulting
mechanism. A program can query whether a device supports concurrent access to
managed memory by checking a new concurrentManagedAccess property. Note,
as with any parallel application, developers need to ensure correct synchronization to
avoid data hazards between processors.
K.2.2.1.GPU Exclusive Access To Managed Memory
To ensure coherency on pre-6.x GPU architectures, the Unified Memory programming
model puts constraints on data accesses while both the CPU and GPU are executing
concurrently. In effect, the GPU has exclusive access to all managed data while any
kernel operation is executing, regardless of whether the specific kernel is actively using
the data. When managed data is used with cudaMemcpy*() or cudaMemset*(), the
system may choose to access the source or destination from the host or the device, which
will put constraints on concurrent CPU access to that data while the cudaMemcpy*()
or cudaMemset*() is executing. See Memcpy()/Memset() Behavior With Managed
Memory for further details.
It is not permitted for the CPU to access any managed allocations or variables while the
GPU is active for devices with concurrentManagedAccess property set to 0. On these
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systems concurrent CPU/GPU accesses, even to different managed memory allocations,
will cause a segmentation fault because the page is considered inaccessible to the CPU.
__device__ __managed__ int x, y=2;
__global__ void kernel() {
x = 10;
}
int main() {
kernel<<< 1, 1 >>>();
y = 20; // Error on GPUs not supporting concurrent access
cudaDeviceSynchronize();
return 0;
}
In example above, the GPU program kernel is still active when the CPU touches y.
(Note how it occurs before cudaDeviceSynchronize().) The code runs successfully
on devices of compute capability 6.x due to the GPU page faulting capability which
lifts all restrictions on simultaneous access. However, such memory access is invalid on
pre-6.x architectures even though the CPU is accessing different data than the GPU. The
program must explicitly synchronize with the GPU before accessing y:
__device__ __managed__ int x, y=2;
__global__ void kernel() {
x = 10;
}
int main() {
kernel<<< 1, 1 >>>();
cudaDeviceSynchronize();
y = 20; // Success on GPUs not supporing concurrent access
return 0;
}
As this example shows, on systems with pre-6.x GPU architectures, a CPU thread may
not access any managed data in between performing a kernel launch and a subsequent
synchronization call, regardless of whether the GPU kernel actually touches that same
data (or any managed data at all). The mere potential for concurrent CPU and GPU
access is sufficient for a process-level exception to be raised.
Note that if memory is dynamically allocated with cudaMallocManaged() or
cuMemAllocManaged() while the GPU is active, the behavior of the memory is
unspecified until additional work is launched or the GPU is synchronized. Attempting
to access the memory on the CPU during this time may or may not cause a segmentation
fault. This does not apply to memory allocated using the flag cudaMemAttachHost or
CU_MEM_ATTACH_HOST.
K.2.2.2.Explicit Synchronization and Logical GPU Activity
Note that explicit synchronization is required even if kernel runs quickly and finishes
before the CPU touches y in the above example. Unified Memory uses logical activity
to determine whether the GPU is idle. This aligns with the CUDA programming
model, which specifies that a kernel can run at any time following a launch and is not
guaranteed to have finished until the host issues a synchronization call.
Any function call that logically guarantees the GPU completes its work is valid.
This includes cudaDeviceSynchronize(); cudaStreamSynchronize() and
cudaStreamQuery() (provided it returns cudaSuccess and not cudaErrorNotReady)
where the specified stream is the only stream still executing on the GPU;
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cudaEventSynchronize() and cudaEventQuery() in cases where the specified
event is not followed by any device work; as well as uses of cudaMemcpy() and
cudaMemset() that are documented as being fully synchronous with respect to the host.
Dependencies created between streams will be followed to infer completion of other
streams by synchronizing on a stream or event. Dependencies can be created via
cudaStreamWaitEvent() or implicitly when using the default (NULL) stream.
It is legal for the CPU to access managed data from within a stream callback, provided
no other stream that could potentially be accessing managed data is active on the
GPU. In addition, a callback that is not followed by any device work can be used for
synchronization: for example, by signaling a condition variable from inside the callback;
otherwise, CPU access is valid only for the duration of the callback(s).
There are several important points of note:
‣It is always permitted for the CPU to access non-managed zero-copy data while the
GPU is active.
‣The GPU is considered active when it is running any kernel, even if that kernel does
not make use of managed data. If a kernel might use data, then access is forbidden,
unless device property concurrentManagedAccess is 1.
‣There are no constraints on concurrent inter-GPU access of managed memory, other
than those that apply to multi-GPU access of non-managed memory.
‣There are no constraints on concurrent GPU kernels accessing managed data.
Note how the last point allows for races between GPU kernels, as is currently the case
for non-managed GPU memory. As mentioned previously, managed memory functions
identically to non-managed memory from the perspective of the GPU. The following
code example illustrates these points:
int main() {
cudaStream_t stream1, stream2;
cudaStreamCreate(&stream1);
cudaStreamCreate(&stream2);
int *non_managed, *managed, *also_managed;
cudaMallocHost(&non_managed, 4); // Non-managed, CPU-accessible memory
cudaMallocManaged(&managed, 4);
cudaMallocManaged(&also_managed, 4);
// Point 1: CPU can access non-managed data.
kernel<<< 1, 1, 0, stream1 >>>(managed);
*non_managed = 1;
// Point 2: CPU cannot access any managed data while GPU is busy,
// unless concurrentManagedAccess = 1
// Note we have not yet synchronized, so "kernel" is still active.
*also_managed = 2; // Will issue segmentation fault
// Point 3: Concurrent GPU kernels can access the same data.
kernel<<< 1, 1, 0, stream2 >>>(managed);
// Point 4: Multi-GPU concurrent access is also permitted.
cudaSetDevice(1);
kernel<<< 1, 1 >>>(managed);
return 0;
}
K.2.2.3.Managing Data Visibility and Concurrent CPU + GPU Access
with Streams
Until now it was assumed that for SM architectures before 6.x: 1) any active kernel may
use any managed memory, and 2) it was invalid to use managed memory from the CPU
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while a kernel is active. Here we present a system for finer-grained control of managed
memory designed to work on all devices supporting managed memory, including older
architectures with concurrentManagedAccess equal to 0.
The CUDA programming model provides streams as a mechanism for programs to
indicate dependence and independence among kernel launches. Kernels launched into
the same stream are guaranteed to execute consecutively, while kernels launched into
different streams are permitted to execute concurrently. Streams describe independence
between work items and hence allow potentially greater efficiency through concurrency.
Unified Memory builds upon the stream-independence model by allowing a CUDA
program to explicitly associate managed allocations with a CUDA stream. In this way,
the programmer indicates the use of data by kernels based on whether they are launched
into a specified stream or not. This enables opportunities for concurrency based on
program-specific data access patterns. The function to control this behaviour is:
cudaError_t cudaStreamAttachMemAsync(cudaStream_t stream,
void *ptr,
size_t length=0,
unsigned int flags=0);
The cudaStreamAttachMemAsync() function associates length bytes of memory
starting from ptr with the specified stream. (Currently, length must always be
0 to indicate that the entire region should be attached.) Because of this association,
the Unified Memory system allows CPU access to this memory region so long as all
operations in stream have completed, regardless of whether other streams are active. In
effect, this constrains exclusive ownership of the managed memory region by an active
GPU to per-stream activity instead of whole-GPU activity.
Most importantly, if an allocation is not associated with a specific stream, it is visible
to all running kernels regardless of their stream. This is the default visibility for a
cudaMallocManaged() allocation or a __managed__ variable; hence, the simple-case
rule that the CPU may not touch the data while any kernel is running.
By associating an allocation with a specific stream, the program makes a guarantee
that only kernels launched into that stream will touch that data. No error checking is
performed by the Unified Memory system: it is the programmer’s responsibility to
ensure that guarantee is honored.
In addition to allowing greater concurrency, the use of cudaStreamAttachMemAsync()
can (and typically does) enable data transfer optimizations within the Unified Memory
system that may affect latencies and other overhead.
K.2.2.4.Stream Association Examples
Associating data with a stream allows fine-grained control over CPU + GPU
concurrency, but what data is visible to which streams must be kept in mind when using
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devices of compute capability lower than 6.x. Looking at the earlier synchronization
example:
__device__ __managed__ int x, y=2;
__global__ void kernel() {
x = 10;
}
int main() {
cudaStream_t stream1;
cudaStreamCreate(&stream1);
cudaStreamAttachMemAsync(stream1, &y, 0, cudaMemAttachHost);
cudaDeviceSynchronize(); // Wait for Host attachment to occur.
kernel<<< 1, 1, 0, stream1 >>>(); // Note: Launches into stream1.
y = 20; // Success – a kernel is running but “y”
// has been associated with no stream.
return 0;
}
Here we explicitly associate y with host accessibility, thus enabling access at all times
from the CPU. (As before, note the absence of cudaDeviceSynchronize() before the
access.) Accesses to y by the GPU running kernel will now produce undefined results.
Note that associating a variable with a stream does not change the associating of any
other variable. E.g. associating x with stream1 does not ensure that only x is accessed
by kernels launched in stream1, thus an error is caused by this code:
__device__ __managed__ int x, y=2;
__global__ void kernel() {
x = 10;
}
int main() {
cudaStream_t stream1;
cudaStreamCreate(&stream1);
cudaStreamAttachMemAsync(stream1, &x);// Associate “x” with stream1.
cudaDeviceSynchronize(); // Wait for “x” attachment to occur.
kernel<<< 1, 1, 0, stream1 >>>(); // Note: Launches into stream1.
y = 20; // ERROR: “y” is still associated
globally
// with all streams by default
return 0;
}
Note how the access to y will cause an error because, even though x has been associated
with a stream, we have told the system nothing about who can see y. The system
therefore conservatively assumes that kernel might access it and prevents the CPU
from doing so.
K.2.2.5.Stream Attach With Multithreaded Host Programs
The primary use for cudaStreamAttachMemAsync() is to enable independent task
parallelism using CPU threads. Typically in such a program, a CPU thread creates its
own stream for all work that it generates because using CUDA’s NULL stream would
cause dependencies between threads.
The default global visibility of managed data to any GPU stream can make it difficult
to avoid interactions between CPU threads in a multi-threaded program. Function
cudaStreamAttachMemAsync() is therefore used to associate a thread’s managed
allocations with that thread’s own stream, and the association is typically not changed
for the life of the thread.
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Such a program would simply add a single call to cudaStreamAttachMemAsync() to
use unified memory for its data accesses:
// This function performs some task, in its own private stream.
void run_task(int *in, int *out, int length) {
// Create a stream for us to use.
cudaStream_t stream;
cudaStreamCreate(&stream);
// Allocate some managed data and associate with our stream.
// Note the use of the host-attach flag to cudaMallocManaged();
// we then associate the allocation with our stream so that
// our GPU kernel launches can access it.
int *data;
cudaMallocManaged((void **)&data, length, cudaMemAttachHost);
cudaStreamAttachMemAsync(stream, data);
cudaStreamSynchronize(stream);
// Iterate on the data in some way, using both Host & Device.
for(int i=0; i<N; i++) {
transform<<< 100, 256, 0, stream >>>(in, data, length);
cudaStreamSynchronize(stream);
host_process(data, length); // CPU uses managed data.
convert<<< 100, 256, 0, stream >>>(out, data, length);
}
cudaStreamSynchronize(stream);
cudaStreamDestroy(stream);
cudaFree(data);
}
In this example, the allocation-stream association is established just once, and then data
is used repeatedly by both the host and device. The result is much simpler code than
occurs with explicitly copying data between host and device, although the result is the
same.
K.2.2.6.Advanced Topic: Modular Programs and Data Access
Constraints
In the previous example cudaMallocManaged() specifies the cudaMemAttachHost
flag, which creates an allocation that is initially invisible to device-side execution. (The
default allocation would be visible to all GPU kernels on all streams.) This ensures that
there is no accidental interaction with another thread’s execution in the interval between
the data allocation and when the data is acquired for a specific stream.
Without this flag, a new allocation would be considered in-use on the GPU if a kernel
launched by another thread happens to be running. This might impact the thread’s
ability to access the newly allocated data from the CPU (for example, within a base-
class constructor) before it is able to explicitly attach it to a private stream. To enable safe
independence between threads, therefore, allocations should be made specifying this
flag.
An alternative would be to place a process-wide barrier across all threads after
the allocation has been attached to the stream. This would ensure that all threads
complete their data/stream associations before any kernels are launched, avoiding
the hazard. A second barrier would be needed before the stream is destroyed
because stream destruction causes allocations to revert to their default visibility. The
cudaMemAttachHost flag exists both to simplify this process, and because it is not
always possible to insert global barriers where required.
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K.2.2.7.Memcpy()/Memset() Behavior With Managed Memory
Since managed memory can be accessed from either the host or the device,
cudaMemcpy*() relies on the type of transfer, specified using cudaMemcpyKind, to
determine whether the data should be accessed as a host pointer or a device pointer.
If cudaMemcpyHostTo* is specified and the source data is managed, then it will
accessed from the host if it is coherently accessible from the host in the copy stream (1);
otherwise it will be accessed from the device. Similar rules apply to the destination when
cudaMemcpy*ToHost is specified and the destination is managed memory.
If cudaMemcpyDeviceTo* is specified and the source data is managed, then it will be
accessed from the device. The source must be coherently accessible from the device
in the copy stream (2); otherwise, an error is returned. Similar rules apply to the
destination when cudaMemcpy*ToDevice is specified and the destination is managed
memory.
If cudaMemcpyDefault is specified, then managed data will be accessed from the host
either if it cannot be coherently accessed from the device in the copy stream (2) or if the
preferred location for the data is cudaCpuDeviceId and it can be coherently accessed
from the host in the copy stream (1); otherwise, it will be accessed from the device.
When using cudaMemset*() with managed memory, the data is always accessed from
the device. The data must be coherently accessible from the device in the stream being
used for the cudaMemset() operation (2); otherwise, an error is returned.
When data is accessed from the device either by cudaMemcpy* or cudaMemset*, the
stream of operation is considered to be active on the GPU. During this time, any CPU
access of data that is associated with that stream or data that has global visibility,
will result in a segmentation fault if the GPU has a zero value for the device attribute
concurrentManagedAccess. The program must synchronize appropriately to ensure
the operation has completed before accessing any associated data from the CPU.
(1) For managed memory to be coherently accessible from the host in a given stream, at
least one of the following conditions must be satisfied:
‣The given stream is associated with a device that has a non-zero value for the device
attribute concurrentManagedAccess.
‣The memory neither has global visibility nor is it associated with the given stream.
(2) For managed memory to be coherently accessible from the device in a given stream,
at least one of the following conditions must be satisfied:
‣The device has a non-zero value for the device attribute
concurrentManagedAccess.
‣The memory either has global visibility or is associated with the given stream.
K.2.3.Language Integration
Users of the CUDA Runtime API who compile their host code using nvcc have access to
additional language integration features, such as shared symbol names and inline kernel
launch via the <<<...>>> operator. Unified Memory adds one additional element to
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CUDA’s language integration: variables annotated with the __managed__ keyword can
be referenced directly from both host and device code.
The following example, seen earlier in Simplifying GPU Programming, illustrates a
simple use of __managed__ global declarations:
// Managed variable declaration is an extra annotation with __device__
__device__ __managed__ int x;
__global__ void kernel() {
// Reference "x" directly - it's a normal variable on the GPU.
printf( "GPU sees: x = %d\n" , x);
}
int main() {
// Set "x" from Host code. Note it's just a normal variable on the CPU.
x = 1234;
// Launch a kernel which uses "x" from the GPU.
kernel<<< 1, 1 >>>();
cudaDeviceSynchronize();
return 0;
}
The capability available with __managed__ variables is that the symbol is available in
both device code and in host code without the need to dereference a pointer, and the
data is shared by all. This makes it particularly easy to exchange data between host and
device programs without the need for explicit allocations or copying.
Semantically, the behavior of __managed__ variables is identical to that of
storage allocated via cudaMallocManaged(). Data is hosted in physical GPU
storage and is visible to all GPUs in the system as well as the CPU. Stream
visibility defaults to cudaMemAttachGlobal, but may be constrained using
cudaStreamAttachMemAsync().
A valid CUDA context is necessary for the correct operation of __managed__ variables.
Accessing __managed__ variables can trigger CUDA context creation if a context for
the current device hasn’t already been created. In the example above, accessing x before
the kernel launch triggers context creation on device 0. In the absence of that access, the
kernel launch would have triggered context creation.
C++ objects declared as __managed__ are subject to certain specific constraints,
particularly where static initializers are concerned. Please refer to C/C++ Language
Support in the CUDA C Programming Guide for a list of these constraints.
K.2.3.1.Host Program Errors with __managed__ Variables
The use of __managed__ variables depends upon the underlying Unified Memory
system functioning correctly. Incorrect functioning can occur if, for example, the CUDA
installation failed or if the CUDA context creation was unsuccessful.
When CUDA-specific operations fail, typically an error is returned that indicates the
source of the failure. Using __managed__ variables introduces a new failure mode
whereby a non-CUDA operation (for example, CPU access to what should be a valid
host memory address) can fail if the Unified Memory system is not operating correctly.
Such invalid memory accesses cannot easily be attributed to the underlying CUDA
subsystem, although a debugger such as cuda-gdb will indicate that a managed
memory address is the source of the failure.
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K.2.4.Querying Unified Memory Support
K.2.4.1.Device Properties
Unified Memory is supported only on devices with compute capability 3.0 or higher.
A program may query whether a GPU device supports managed memory by using
cudaGetDeviceProperties() and checking the new managedMemory property.
The capability can also be determined using the individual attribute query function
cudaDeviceGetAttribute() with the attribute cudaDevAttrManagedMemory.
Either property will be set to 1 if managed memory allocations are permitted on
the GPU and under the current operating system. Note that Unified Memory is not
supported for 32-bit applications (unless on Android), even if a GPU is of sufficient
capability.
Devices of compute capability 6.x on supporting platforms can access pageable
memory without calling cudaHostRegister on it. An application can query whether
the device supports coherently accessing pageable memory by checking the new
pageableMemoryAccess property.
With the new page fault mechanism, global data coherency is guaranteed with Unified
Memory. This means that the CPUs and GPUs can access Unified Memory allocations
simultaneously. This was illegal on devices of compute capability lower than 6.x,
because coherence could not be guaranteed if the CPU accessed a Unified Memory
allocation while a GPU kernel was active. A program can query concurrent access
support by checking concurrentManagedAccess property. See Coherency and
Concurrency for details.
K.2.4.2.Pointer Attributes
To determine if a given pointer refers to managed memory, a program can call
cudaPointerGetAttributes() and check the value of the isManaged attribute. This
attribute is set to 1 if the pointer refers to managed memory and to 0 if not.
K.2.5.Advanced Topics
K.2.5.1.Managed Memory with Multi-GPU Programs on pre-6.x
Architectures
On systems with devices of compute capabilities lower than 6.x managed allocations
are automatically visible to all GPUs in a system via the peer-to-peer capabilities of the
GPUs.
On Linux the managed memory is allocated in GPU memory as long as all GPUs that
are actively being used by a program have the peer-to-peer support. If at any time the
application starts using a GPU that doesn’t have peer-to-peer support with any of the
other GPUs that have managed allocations on them, then the driver will migrate all
managed allocations to system memory.
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On Windows if peer mappings are not available (for example, between GPUs of
different architectures), then the system will automatically fall back to using zero-copy
memory, regardless of whether both GPUs are actually used by a program. If only one
GPU is actually going to be used, it is necessary to set the CUDA_VISIBLE_DEVICES
environment variable before launching the program. This constrains which GPUs are
visible and allows managed memory to be allocated in GPU memory.
Alternatively, on Windows users can also set CUDA_MANAGED_FORCE_DEVICE_ALLOC to
a non-zero value to force the driver to always use device memory for physical storage.
When this environment variable is set to a non-zero value, all devices used in that
process that support managed memory have to be peer-to-peer compatible with each
other. The error ::cudaErrorInvalidDevice will be returned if a device that supports
managed memory is used and it is not peer-to-peer compatible with any of the other
managed memory supporting devices that were previously used in that process, even
if ::cudaDeviceReset has been called on those devices. These environment variables are
described in Appendix CUDA Environment Variables. Note that starting from CUDA
8.0 CUDA_MANAGED_FORCE_DEVICE_ALLOC has no effect on Linux operating systems.
K.2.5.2.Using fork() with Managed Memory
The Unified Memory system does not allow sharing of managed memory pointers
between processes. It will not correctly manage memory handles that have been
duplicated via a fork() operation. Results will be undefined if either the child or parent
accesses managed data following a fork().
It is safe, however, to fork() a child process that then immediately exits via an exec()
call, because the child drops the memory handles and the parent becomes the sole owner
once again. It is not safe for the parent to exit and leave the child to access the handles.
K.3.Performance Tuning
In order to achieve good performance with Unified Memory, the following objectives
must be met:
‣Faults should be avoided: While replayable faults are fundamental to enabling a
simpler programming model, they can be severely detrimental to application
performance. Fault handling can take tens of microseconds because it may involve
TLB invalidates, data migrations and page table updates. All the while, execution
in certain portions of the application will be halted, thereby potentially impacting
overall performance.
‣Data should be local to the accessing processor: As mentioned before, memory access
latencies and bandwidth are significantly better when the data is placed local to
the processor accessing it. Therefore, data should be suitably migrated to take
advantage of lower latencies and higher bandwidth.
‣Memory thrashing should be prevented: If data is frequently accessed by multiple
processors and has to be constantly migrated around to achieve data locality, then
the overhead of migration may exceed the benefits of locality. Memory thrashing
should be prevented to the extent possible. If it cannot be prevented, it must be
detected and resolved appropriately.
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To achieve the same level of performance as what's possible without using Unified
Memory, the application has to guide the Unified Memory driver subsystem into
avoiding the aforementioned pitfalls. It is worthy to note that the Unified Memory
driver subsystem can detect common data access patterns and achieve some of these
objectives automatically without application participation. But when the data access
patterns are non-obvious, explicit guidance from the application is crucial. CUDA
8.0 introduces useful APIs for providing the runtime with memory usage hints
(cudaMemAdvise()) and for explicit prefetching (cudaMemPrefetchAsync()). These
tools allow the same capabilities as explicit memory copy and pinning APIs without
reverting to the limitations of explicit GPU memory allocation.
K.3.1.Data Prefetching
Data prefetching means migrating data to a processor’s memory and mapping it in
that processor’s page tables before the processor begins accessing that data. The intent
of data prefetching is to avoid faults while also establishing data locality. This is most
valuable for applications that access data primarily from a single processor at any given
time. As the accessing processor changes during the lifetime of the application, the data
can be prefetched accordingly to follow the execution flow of the application. Since work
is launched in streams in CUDA, it is expected of data prefetching to also be a streamed
operation as shown in the following API:
cudaError_t cudaMemPrefetchAsync(const void *devPtr,
size_t count,
int dstDevice,
cudaStream_t stream);
where the memory region specified by devPtr pointer and count number of bytes, with
ptr rounded down to the nearest page boundary and count rounded up to the nearest
page boundary, is migrated to the dstDevice by enqueueing a migration operation in
stream. Passing in cudaCpuDeviceId for dstDevice will cause data to be migrated to
CPU memory.
Consider a simple code example below:
void foo(cudaStream_t s) {
char *data;
cudaMallocManaged(&data, N);
init_data(data, N); // execute on CPU
cudaMemPrefetchAsync(data, N, myGpuId, s); // prefetch to GPU
mykernel<<<..., s>>>(data, N, 1, compare); // execute on GPU
cudaMemPrefetchAsync(data, N, cudaCpuDeviceId, s); // prefetch to CPU
cudaStreamSynchronize(s);
use_data(data, N);
cudaFree(data);
}
Without performance hints the kernel mykernel will fault on first access to data
which creates additional overhead of the fault processing and generally slows down
the application. By prefetching data in advance it is possible to avoid page faults and
achieve better performance.
This API follows stream ordering semantics, i.e. the migration does not begin until all
prior operations in the stream have completed, and any subsequent operation in the
stream does not begin until the migration has completed.
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K.3.2.Data Usage Hints
Data prefetching alone is insufficient when multiple processors need to simultaneously
access the same data. In such scenarios, it's useful for the application to provide hints on
how the data will actually be used. The following advisory API can be used to specify
data usage:
cudaError_t cudaMemAdvise(const void *devPtr,
size_t count,
enum cudaMemoryAdvise advice,
int device);
where advice, specified for data contained in region starting from devPtr address
and with the length of count bytes, rounded to the nearest page boundary, can take the
following values:
‣cudaMemAdviseSetReadMostly: This implies that the data is mostly going to be
read from and only occasionally written to. This allows the driver to create read-
only copies of the data in a processor's memory when that processor accesses it.
Similarly, if cudaMemPrefetchAsync is called on this region, it will create a read-
only copy of the data on the destination processor. When a processor writes to this
data, all copies of the corresponding page are invalidated except for the one where
the write occurred. The device argument is ignored for this advice. This advice
allows multiple processors to simultaneously access the same data at maximal
bandwidth as illustrated in the following code snippet:
char *dataPtr;
size_t dataSize = 4096;
// Allocate memory using malloc or cudaMallocManaged
dataPtr = (char *)malloc(dataSize);
// Set the advice on the memory region
cudaMemAdvise(dataPtr, dataSize, cudaMemAdviseSetReadMostly, 0);
int outerLoopIter = 0;
while (outerLoopIter < maxOuterLoopIter) {
// The data is written to in the outer loop on the CPU
initializeData(dataPtr, dataSize);
// The data is made available to all GPUs by prefetching.
// Prefetching here causes read duplication of data instead
// of data migration
for (int device = 0; device < maxDevices; device++) {
cudaMemPrefetchAsync(dataPtr, dataSize, device, stream);
}
// The kernel only reads this data in the inner loop
int innerLoopIter = 0;
while (innerLoopIter < maxInnerLoopIter) {
kernel<<<32,32>>>((const char *)dataPtr);
innerLoopIter++;
}
outerLoopIter++;
}
‣cudaMemAdviseSetPreferredLocation: This advice sets the preferred
location for the data to be the memory belonging to device. Passing in a value of
cudaCpuDeviceId for device sets the preferred location as CPU memory. Setting
the preferred location does not cause data to migrate to that location immediately.
Instead, it guides the migration policy when a fault occurs on that memory region. If
the data is already in its preferred location and the faulting processor can establish
a mapping without requiring the data to be migrated, then the migration will be
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avoided. On the other hand, if the data is not in its preferred location or if a direct
mapping cannot be established, then it will be migrated to the processor accessing
it. It is important to note that setting the preferred location does not prevent data
prefetching done using cudaMemPrefetchAsync.
‣cudaMemAdviseSetAccessedBy: This advice implies that the data will be accessed
by device. This does not cause data migration and has no impact on the location
of the data per se. Instead, it causes the data to always be mapped in the specified
processor’s page tables, as long as the location of the data permits a mapping to
be established. If the data gets migrated for any reason, the mappings are updated
accordingly. This advice is useful in scenarios where data locality is not important,
but avoiding faults is. Consider for example a system containing multiple GPUs
with peer-to-peer access enabled, where the data located on one GPU is occasionally
accessed by other GPUs. In such scenarios, migrating data over to the other GPUs is
not as important because the accesses are infrequent and the overhead of migration
may be too high. But preventing faults can still help improve performance, and
so having a mapping set up in advance is useful. Note that on CPU access of this
data, the data may be migrated to CPU memory because the CPU cannot access
GPU memory directly. Any GPU that had the cudaMemAdviceSetAccessedBy flag
set for this data will now have its mapping updated to point to the page in CPU
memory.
Each advice can be also unset by using one of the following values:
cudaMemAdviseUnsetReadMostly, cudaMemAdviseUnsetPreferredLocation and
cudaMemAdviseUnsetAccessedBy.
K.3.3.Querying Usage Attributes
A program can query memory range attributes assigned through cudaMemAdvise or
cudaMemPrefetchAsync by using the following API:
cudaMemRangeGetAttribute(void *data,
size_t dataSize,
enum cudaMemRangeAttribute attribute,
const void *devPtr,
size_t count);
This function queries an attribute of the memory range starting at devPtr with a
size of count bytes. The memory range must refer to managed memory allocated via
cudaMallocManaged or declared via __managed__ variables. It is possible to query the
following attributes:
‣cudaMemRangeAttributeReadMostly: the result returned will be 1 if all pages in
the given memory range have read-duplication enabled, or 0 otherwise.
‣cudaMemRangeAttributePreferredLocation: the result returned will
be a GPU device id or cudaCpuDeviceId if all pages in the memory range
have the corresponding processor as their preferred location, otherwise
cudaInvalidDeviceId will be returned. An application can use this query API to
make decision about staging data through CPU or GPU depending on the preferred
location attribute of the managed pointer. Note that the actual location of the pages
in the memory range at the time of the query may be different from the preferred
location.
Unified Memory Programming
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CUDA C Programming Guide PG-02829-001_v9.1|286
‣cudaMemRangeAttributeAccessedBy: will return the list of devices that have that
advise set for that memory range.
‣cudaMemRangeAttributeLastPrefetchLocation: will return the last
location to which all pages in the memory range were prefetched explicitly using
cudaMemPrefetchAsync. Note that this simply returns the last location that the
application requested to prefetch the memory range to. It gives no indication as to
whether the prefetch operation to that location has completed or even begun.
Additionally, multiple attributes can be queried by using corresponding
cudaMemRangeGetAttributes function.
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