NVIDIA CUDA Programming Guide
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NVIDIA CUDA™
NVIDIA CUDA C
Programming Guide
Version 4.2
4/16/2012
Changes from Version 4.1
Updated Chapter 4, Chapter 5, and Appendix F to include information on
devices of compute capability 3.0.
Replaced each reference to “processor core” with “multiprocessor” in
Section 1.3.
Replaced Table A-1 by a reference to http://developer.nvidia.com/cuda-gpus.
Added new Section B.13 on the warp shuffle functions.
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CUDA C Programming Guide Version 4.2
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 Architecture .......................... 3
1.3
A Scalable Programming Model .................................................................... 4
1.4
Document’s Structure ................................................................................. 6
Chapter 2. Programming Model ....................................................................... 7
2.1
Kernels ...................................................................................................... 7
2.2
Thread Hierarchy ........................................................................................ 8
2.3
Memory Hierarchy .................................................................................... 10
2.4
Heterogeneous Programming .................................................................... 11
2.5
Compute Capability ................................................................................... 14
Chapter 3. Programming Interface ................................................................ 15
3.1
Compilation with NVCC ............................................................................. 15
3.1.1
Compilation Workflow......................................................................... 16
3.1.1.1
Offline Compilation ...................................................................... 16
3.1.1.2
Just-in-Time Compilation .............................................................. 16
3.1.2
Binary Compatibility ........................................................................... 17
3.1.3
PTX Compatibility ............................................................................... 17
3.1.4
Application Compatibility ..................................................................... 17
3.1.5
C/C++ Compatibility .......................................................................... 18
3.1.6
64-Bit Compatibility ............................................................................ 18
3.2
CUDA C Runtime ...................................................................................... 19
3.2.1
Initialization....................................................................................... 19
3.2.2
Device Memory .................................................................................. 20
3.2.3
Shared Memory ................................................................................. 22
3.2.4
Page-Locked Host Memory.................................................................. 28
3.2.4.1
Portable Memory ......................................................................... 29
3.2.4.2
Write-Combining Memory ............................................................. 29
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3.2.4.3
3.2.5
Mapped Memory.......................................................................... 29
Asynchronous Concurrent Execution .................................................... 30
3.2.5.1
Concurrent Execution between Host and Device ............................. 30
3.2.5.2
Overlap of Data Transfer and Kernel Execution .............................. 30
3.2.5.3
Concurrent Kernel Execution ........................................................ 31
3.2.5.4
Concurrent Data Transfers ........................................................... 31
3.2.5.5
Streams ...................................................................................... 31
3.2.5.6
Events ........................................................................................ 34
3.2.5.7
Synchronous Calls ....................................................................... 34
3.2.6
Multi-Device System ........................................................................... 35
3.2.6.1
Device Enumeration ..................................................................... 35
3.2.6.2
Device Selection .......................................................................... 35
3.2.6.3
Stream and Event Behavior .......................................................... 35
3.2.6.4
Peer-to-Peer Memory Access ........................................................ 36
3.2.6.5
Peer-to-Peer Memory Copy........................................................... 36
3.2.7
Unified Virtual Address Space .............................................................. 37
3.2.8
Error Checking ................................................................................... 37
3.2.9
Call Stack .......................................................................................... 38
3.2.10
Texture and Surface Memory .............................................................. 38
3.2.10.1
Texture Memory .......................................................................... 38
3.2.10.2
Surface Memory .......................................................................... 45
3.2.10.3
CUDA Arrays ............................................................................... 48
3.2.10.4
Read/Write Coherency ................................................................. 48
3.2.11
Graphics Interoperability..................................................................... 48
3.2.11.1
OpenGL Interoperability ............................................................... 49
3.2.11.2
Direct3D Interoperability .............................................................. 51
3.2.11.3
SLI Interoperability ...................................................................... 58
3.3
Versioning and Compatibility...................................................................... 58
3.4
Compute Modes ....................................................................................... 59
3.5
Mode Switches ......................................................................................... 60
3.6
Tesla Compute Cluster Mode for Windows .................................................. 60
Chapter 4. Hardware Implementation ........................................................... 61
4.1
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SIMT Architecture ..................................................................................... 61
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4.2
Hardware Multithreading ........................................................................... 62
Chapter 5. Performance Guidelines ............................................................... 65
5.1
Overall Performance Optimization Strategies ............................................... 65
5.2
Maximize Utilization .................................................................................. 65
5.2.1
Application Level ................................................................................ 65
5.2.2
Device Level ...................................................................................... 66
5.2.3
Multiprocessor Level ........................................................................... 66
5.3
Maximize Memory Throughput ................................................................... 68
5.3.1
Data Transfer between Host and Device .............................................. 69
5.3.2
Device Memory Accesses .................................................................... 70
5.4
5.3.2.1
Global Memory ............................................................................ 70
5.3.2.2
Local Memory.............................................................................. 72
5.3.2.3
Shared Memory ........................................................................... 72
5.3.2.4
Constant Memory ........................................................................ 73
5.3.2.5
Texture and Surface Memory ........................................................ 73
Maximize Instruction Throughput ............................................................... 73
5.4.1
Arithmetic Instructions ....................................................................... 74
5.4.2
Control Flow Instructions .................................................................... 77
5.4.3
Synchronization Instruction ................................................................. 77
Appendix A. CUDA-Enabled GPUs .................................................................. 79
Appendix B. C Language Extensions .............................................................. 81
B.1
Function Type Qualifiers ............................................................................ 81
B.1.1
__device__ ........................................................................................ 81
B.1.2
__global__ ........................................................................................ 81
B.1.3
__host__ ........................................................................................... 81
B.1.4
__noinline__ and __forceinline__ ........................................................ 82
B.2
Variable Type Qualifiers ............................................................................ 82
B.2.1
__device__ ........................................................................................ 83
B.2.2
__constant__ ..................................................................................... 83
B.2.3
__shared__ ....................................................................................... 83
B.2.4
__restrict__ ....................................................................................... 84
B.3
Built-in Vector Types ................................................................................. 85
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B.3.1
char1, uchar1, char2, uchar2, char3, uchar3, char4, uchar4, short1,
ushort1, short2, ushort2, short3, ushort3, short4, ushort4, int1, uint1, int2, uint2,
int3, uint3, int4, uint4, long1, ulong1, long2, ulong2, long3, ulong3, long4, ulong4,
longlong1, ulonglong1, longlong2, ulonglong2, float1, float2, float3, float4, double1,
double2 85
B.3.2
B.4
Built-in Variables ...................................................................................... 86
B.4.1
gridDim ............................................................................................. 87
B.4.2
blockIdx ............................................................................................ 87
B.4.3
blockDim ........................................................................................... 87
B.4.4
threadIdx .......................................................................................... 87
B.4.5
warpSize ........................................................................................... 87
B.5
Memory Fence Functions ........................................................................... 87
B.6
Synchronization Functions ......................................................................... 89
B.7
Mathematical Functions ............................................................................. 89
B.8
Texture Functions ..................................................................................... 90
B.8.1
tex1Dfetch() ...................................................................................... 90
B.8.2
tex1D() ............................................................................................. 91
B.8.3
tex2D() ............................................................................................. 91
B.8.4
tex3D() ............................................................................................. 91
B.8.5
tex1DLayered() .................................................................................. 91
B.8.6
tex2DLayered() .................................................................................. 91
B.8.7
texCubemap().................................................................................... 92
B.8.8
texCubemapLayered() ........................................................................ 92
B.8.9
tex2Dgather() .................................................................................... 92
B.9
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dim3 ................................................................................................. 86
Surface Functions ..................................................................................... 92
B.9.1
surf1Dread() ...................................................................................... 92
B.9.2
surf1Dwrite() ..................................................................................... 93
B.9.3
surf2Dread() ...................................................................................... 93
B.9.4
surf2Dwrite() ..................................................................................... 93
B.9.5
surf3Dread() ...................................................................................... 93
B.9.6
surf3Dwrite() ..................................................................................... 94
B.9.7
surf1DLayeredread() .......................................................................... 94
B.9.8
surf1DLayeredwrite() ......................................................................... 94
CUDA C Programming Guide Version 4.2
B.9.9
surf2DLayeredread() .......................................................................... 94
B.9.10
surf2DLayeredwrite() ......................................................................... 95
B.9.11
surfCubemapread() ............................................................................ 95
B.9.12
surfCubemapwrite() ........................................................................... 95
B.9.13
surfCubemabLayeredread() ................................................................. 95
B.9.14
surfCubemapLayeredwrite() ................................................................ 96
B.10
Time Function .......................................................................................... 96
B.11
Atomic Functions ...................................................................................... 96
B.11.1
Arithmetic Functions ........................................................................... 97
B.11.1.1
atomicAdd() ................................................................................ 97
B.11.1.2
atomicSub() ................................................................................ 97
B.11.1.3
atomicExch() ............................................................................... 98
B.11.1.4
atomicMin() ................................................................................ 98
B.11.1.5
atomicMax()................................................................................ 98
B.11.1.6
atomicInc() ................................................................................. 98
B.11.1.7
atomicDec() ................................................................................ 98
B.11.1.8
atomicCAS() ................................................................................ 99
B.11.2
Bitwise Functions ............................................................................... 99
B.11.2.1
atomicAnd() ................................................................................ 99
B.11.2.2
atomicOr() .................................................................................. 99
B.11.2.3
atomicXor()................................................................................. 99
B.12
Warp Vote Functions............................................................................... 100
B.13
Warp Shuffle Functions ........................................................................... 100
B.13.1
Synopsys ......................................................................................... 100
B.13.2
Description ...................................................................................... 100
B.13.3
Return Value ................................................................................... 101
B.13.4
Notes .............................................................................................. 101
B.13.5
Examples ........................................................................................ 102
B.13.5.1
Broadcast of a single value across a warp .................................... 102
B.13.5.2
Inclusive plus-scan across sub-partitions of 8 threads ................... 102
B.13.5.3
Reduction across a warp ............................................................ 103
B.14
Profiler Counter Function ......................................................................... 103
B.15
Assertion ............................................................................................... 103
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B.16
Formatted Output ................................................................................... 104
B.16.1
Format Specifiers ............................................................................. 105
B.16.2
Limitations ...................................................................................... 105
B.16.3
Associated Host-Side API .................................................................. 106
B.16.4
Examples ........................................................................................ 106
B.17
Dynamic Global Memory Allocation ........................................................... 108
B.17.1
Heap Memory Allocation ................................................................... 108
B.17.2
Interoperability with Host Memory API ............................................... 109
B.17.3
Examples ........................................................................................ 109
B.17.3.1
Per Thread Allocation ................................................................. 109
B.17.3.2
Per Thread Block Allocation ........................................................ 109
B.17.3.3
Allocation Persisting Between Kernel Launches ............................. 110
B.18
Execution Configuration .......................................................................... 111
B.19
Launch Bounds ....................................................................................... 112
B.20
#pragma unroll ...................................................................................... 114
Appendix C. Mathematical Functions ........................................................... 115
C.1
Standard Functions ................................................................................. 115
C.1.1
Single-Precision Floating-Point Functions ............................................ 115
C.1.2
Double-Precision Floating-Point Functions .......................................... 118
C.2
Intrinsic Functions .................................................................................. 120
C.2.1
Single-Precision Floating-Point Functions ............................................ 121
C.2.2
Double-Precision Floating-Point Functions .......................................... 122
Appendix D. C/C++ Language Support ....................................................... 123
D.1
Code Samples ........................................................................................ 123
D.1.1
Data Aggregation Class .................................................................... 123
D.1.2
Derived Class ................................................................................... 124
D.1.3
Class Template ................................................................................ 124
D.1.4
Function Template ........................................................................... 125
D.1.5
Functor Class ................................................................................... 125
D.2
Restrictions ............................................................................................ 126
D.2.1
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Qualifiers......................................................................................... 126
D.2.1.1
Device Memory Qualifiers ........................................................... 126
D.2.1.2
Volatile Qualifier ........................................................................ 126
CUDA C Programming Guide Version 4.2
D.2.2
Pointers .......................................................................................... 127
D.2.3
Operators ........................................................................................ 127
D.2.3.1
Assignment Operator ................................................................. 127
D.2.3.2
Address Operator ...................................................................... 127
D.2.4
Functions ........................................................................................ 127
D.2.4.1
Function Parameters .................................................................. 127
D.2.4.2
Static Variables within Function .................................................. 128
D.2.4.3
Function Pointers....................................................................... 128
D.2.4.4
Function Recursion .................................................................... 128
D.2.5
Classes............................................................................................ 128
D.2.5.1
Data Members........................................................................... 128
D.2.5.2
Function Members ..................................................................... 128
D.2.5.3
Constructors and Destructors ..................................................... 128
D.2.5.4
Virtual Functions ....................................................................... 128
D.2.5.5
Virtual Base Classes ................................................................... 128
D.2.5.6
Windows-Specific ...................................................................... 128
D.2.6
Templates ....................................................................................... 129
Appendix E. Texture Fetching ...................................................................... 131
E.1
Nearest-Point Sampling ........................................................................... 132
E.2
Linear Filtering ....................................................................................... 132
E.3
Table Lookup ......................................................................................... 134
Appendix F. Compute Capabilities ............................................................... 135
F.1
Features and Technical Specifications ....................................................... 136
F.2
Floating-Point Standard ........................................................................... 139
F.3
Compute Capability 1.x ........................................................................... 141
F.3.1
Architecture ..................................................................................... 141
F.3.2
Global Memory ................................................................................ 141
F.3.2.1
Devices of Compute Capability 1.0 and 1.1 .................................. 142
F.3.2.2
Devices of Compute Capability 1.2 and 1.3 .................................. 142
F.3.3
Shared Memory ............................................................................... 143
F.3.3.1
32-Bit Strided Access ................................................................. 143
F.3.3.2
32-Bit Broadcast Access ............................................................. 143
F.3.3.3
8-Bit and 16-Bit Access .............................................................. 144
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F.3.3.4
F.4
Larger Than 32-Bit Access .......................................................... 144
Compute Capability 2.x ........................................................................... 145
F.4.1
Architecture ..................................................................................... 145
F.4.2
Global Memory ................................................................................ 146
F.4.3
Shared Memory ............................................................................... 147
F.4.3.1
32-Bit Strided Access ................................................................. 147
F.4.3.2
Larger Than 32-Bit Access .......................................................... 148
F.4.4
F.5
Constant Memory ............................................................................. 148
Compute Capability 3.0 ........................................................................... 149
F.5.1
Architecture ..................................................................................... 149
F.5.2
Global Memory ................................................................................ 150
F.5.3
Shared Memory ............................................................................... 152
F.5.3.1
64-Bit Mode .............................................................................. 152
F.5.3.2
32-Bit Mode .............................................................................. 152
Appendix G. Driver API ................................................................................ 155
x
G.1
Context.................................................................................................. 157
G.2
Module .................................................................................................. 158
G.3
Kernel Execution..................................................................................... 158
G.4
Interoperability between Runtime and Driver APIs ..................................... 160
CUDA C Programming Guide Version 4.2
List of Figures
Figure 1-1. Floating-Point Operations per Second and Memory Bandwidth for the CPU
and GPU 2
Figure 1-2.
The GPU Devotes More Transistors to Data Processing ............................ 3
Figure 1-3. CUDA is Designed to Support Various Languages and Application
Programming Interfaces .................................................................................... 4
Figure 1-4.
Automatic Scalability ............................................................................ 5
Figure 2-1.
Grid of Thread Blocks ........................................................................... 9
Figure 2-2.
Memory Hierarchy .............................................................................. 11
Figure 2-3.
Heterogeneous Programming .............................................................. 13
Figure 3-1.
Matrix Multiplication without Shared Memory ........................................ 24
Figure 3-2.
Matrix Multiplication with Shared Memory ............................................ 28
Figure 3-3.
The Driver API is Backward, but Not Forward Compatible ...................... 59
Figure E-1.
Nearest-Point Sampling of a One-Dimensional Texture of Four Texels .. 132
Figure E-2. Linear Filtering of a One-Dimensional Texture of Four Texels in Clamp
Addressing Mode........................................................................................... 133
Figure E-3.
One-Dimensional Table Lookup Using Linear Filtering .......................... 134
Figure F-1. Examples of Global Memory Accesses by a Warp, 4-Byte Word per Thread,
and Associated Memory Transactions Based on Compute Capability .................. 151
Figure F-2 Examples of Strided Shared Memory Accesses for Devices of Compute
Capability 3.0................................................................................................ 153
Figure F-3 Examples of Irregular Shared Memory Accesses for Devices of Compute
Capability 3.0................................................................................................ 154
Figure G-1
Library Context Management ............................................................ 158
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xi
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 1-1.
CUDA C Programming Guide Version 4.2
1
Chapter 1. Introduction
Figure 1-1. Floating-Point Operations per Second and
Memory Bandwidth for the CPU and GPU
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CUDA C Programming Guide Version 4.2
Chapter 1. Introduction
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 1-2.
Control
ALU
ALU
ALU
ALU
Cache
DRAM
DRAM
CPU
GPU
Figure 1-2. 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, 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 Architecture
In November 2006, NVIDIA introduced CUDA™, a general purpose parallel
computing architecture – with a new parallel programming model and instruction
set architecture – that leverages the parallel compute engine in NVIDIA GPUs to
CUDA C Programming Guide Version 4.2
3
Chapter 1. Introduction
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 1-3, other languages,
application programming interfaces, or directives-based approaches are supported,
such as FORTRAN, DirectCompute, OpenCL, OpenACC.
Figure 1-3. 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.
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.
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CUDA C Programming Guide Version 4.2
Chapter 1. Introduction
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 1-4, and only the runtime system needs to know the physical
multiprocessor count.
This scalable programming model allows the CUDA 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 Appendix A for a list of all CUDA-enabled GPUs).
Multithreaded CUDA Program
Block 0
Block 1
Block 2
Block 3
Block 4
Block 5
Block 5
Block 6
Block 6
Block 7
GPU with 2 SMs
GPU with 4 SMs
SM 0
SM 1
SM 0
SM 1
SM 2
SM 3
Block 0
Block 1
Block 0
Block 1
Block 2
Block 3
Block 2
Block 3
Block 4
Block 5
Block 6
Block 7
Block 4
Block 5
Block 6
Block 7
A GPU is built around an array of Streaming Multiprocessors (SMs) (see Chapter 4 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 1-4. Automatic Scalability
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Chapter 1. Introduction
1.4
Document’s Structure
This document is organized into the following chapters:
6
Chapter 1 is a general introduction to CUDA.
Chapter 2 outlines the CUDA programming model.
Chapter 3 describes the programming interface.
Chapter 4 describes the hardware implementation.
Chapter 5 gives some guidance on how to achieve maximum performance.
Appendix A lists all CUDA-enabled devices.
Appendix B is a detailed description of all extensions to the C language.
Appendix C lists the mathematical functions supported in CUDA.
Appendix D lists the C++ features supported in device code.
Appendix E gives more details on texture fetching.
Appendix F gives the technical specifications of various devices, as well as more
architectural details.
Appendix G introduces the low-level driver API.
CUDA C Programming Guide Version 4.2
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 Chapter 3.
Full code for the vector addition example used in this chapter and the next can be
found in the vectorAdd SDK code 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 Appendix B.18). 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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Chapter 2. Programming Model
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 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 threedimensional 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<<>>(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 2-1. 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 (0, 0)
Block (1, 0)
Block (2, 0)
Block (0, 1)
Block (1, 1)
Block (2, 1)
Block (1, 1)
Thread (0, 0) Thread (1, 0) Thread (2, 0) Thread (3, 0)
Thread (0, 1) Thread (1, 1) Thread (2, 1) Thread (3, 1)
Thread (0, 2) Thread (1, 2) Thread (2, 2) Thread (3, 2)
Figure 2-1. 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.
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];
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}
int main()
{
...
// Kernel invocation
dim3 threadsPerBlock(16, 16);
dim3 numBlocks(N / threadsPerBlock.x, N / threadsPerBlock.y);
MatAdd<<>>(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 1-4, 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.
Section 3.2.3 gives an example of using shared memory.
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 2-2. 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 Sections 5.3.2.1, 5.3.2.4, and
5.3.2.5). Texture memory also offers different addressing modes, as well as data
filtering, for some specific data formats (see Section 3.2.10).
The global, constant, and texture memory spaces are persistent across kernel
launches by the same application.
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Thread
Per-thread local
memory
Thread Block
Per-block shared
memory
Grid 0
Block (0, 0)
Block (1, 0)
Block (2, 0)
Block (0, 1)
Block (1, 1)
Block (2, 1)
Grid 1
Global memory
Block (0, 0)
Block (1, 0)
Block (0, 1)
Block (1, 1)
Block (0, 2)
Block (1, 2)
Figure 2-2. Memory Hierarchy
2.4
Heterogeneous Programming
As illustrated by Figure 2-3, 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.
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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 Chapter 3). This includes device memory allocation and deallocation as
well as data transfer between host and device memory.
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C Program
Sequential
Execution
Serial code
Host
Parallel kernel
Device
Kernel0<<<>>>()
Grid 0
Serial code
Parallel kernel
Kernel1<<<>>>()
Block (0, 0)
Block (1, 0)
Block (2, 0)
Block (0, 1)
Block (1, 1)
Block (2, 1)
Host
Device
Grid 1
Block (0, 0)
Block (1, 0)
Block (0, 1)
Block (1, 1)
Block (0, 2)
Block (1, 2)
Serial code executes on the host while parallel code executes on the device.
Figure 2-3. Heterogeneous Programming
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2.5
Compute Capability
The compute capability of a device is defined by a major revision number and a minor
revision number.
Devices with the same major revision number are of the same core architecture. The
major revision number is 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.
Appendix A lists of all CUDA-enabled devices along with their compute capability.
Appendix F gives the technical specifications of each compute capability.
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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 Chapter 2. 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 Appendix B. Any source file that
contains some of these extensions must be compiled with nvcc as outlined in
Section 3.1.
The runtime is introduced in Section 3.2. 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 Appendix G 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 an overview of nvcc workflow and command options. A complete
description can be found in the nvcc user manual.
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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
Section 2.1 (and described in more details in Section B.18) 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,
Or ignore the modifed host code (if any) and use the CUDA driver API (see
Appendix G) 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 applications to benefit from
latest compiler improvements. 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
Section 3.1.4.
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 justin-time compiler built into the device driver.
Environment variables are available to control just-in-time compilation:
Setting CUDA_CACHE_DISABLE to 1 disables caching (i.e. no binary code is
added to or retrieved from the cache).
CUDA_CACHE_MAXSIZE specifies the size of the compute cache in bytes; the
default size is 32 MB and the maximum size is 4 GB; 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.
CUDA_CACHE_PATH specifies the folder where the compute cache files are
stored; the default values are:
on Windows, %APPDATA%\NVIDIA\ComputeCache,
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on MacOS,
$HOME/Library/Application\ Support/NVIDIA/ComputeCach
e,
3.1.2
on Linux, ~/.nv/ComputeCache.
Setting CUDA_FORCE_PTX_JIT to 1 forces the device driver to ignore any
binary code embedded in an application (see Section 3.1.4) 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.
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_13 produces binary code for devices of compute capability 1.3. Binary
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 is only guaranteed to 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, atomic instructions on global memory are only supported
on devices of compute capability 1.1 and above; double-precision instructions are
only supported on devices of compute capability 1.3 and above. The –arch
compiler option specifies the compute capability that is assumed when compiling C
to PTX code. So, code that contains double-precision arithmetic, for example, must
be compiled with “-arch=sm_13” (or higher compute capability), otherwise
double-precision arithmetic will get demoted to single-precision arithmetic.
PTX code produced for some specific compute capability can always be compiled to
binary code of greater or equal compute capability.
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
Sections 3.1.2 and 3.1.3. 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 Section 3.1.1.2).
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,
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nvcc x.cu
–gencode arch=compute_10,code=sm_10
–gencode arch=compute_11,code=\’compute_11,sm_11\’
embeds binary code compatible with compute capability 1.0 (first –gencode
option) and PTX and binary code compatible with compute capability 1.1 (second
-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:
1.0 binary code for devices with compute capability 1.0,
1.1 binary code for devices with compute capability 1.1, 1.2, 1.3,
binary code obtained by compiling 1.1 PTX code for devices with compute
capabilities 2.0 and higher.
x.cu can have an optimized code path that uses atomic operations, for example,
which are only supported in devices of compute capability 1.1 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_11” for example, __CUDA_ARCH__ is equal to 110.
Applications using the driver API must compile code to separate files and explicitly
load and execute the most appropriate file at runtime.
The nvcc user manual lists various shorthands for the –arch, –code, and
gencode compiler options. For example, “arch=sm_13” is a shorthand for
“arch=compute_13 code=compute_13,sm_13” (which is the same as
“gencode arch=compute_13,code=\’compute_13,sm_13\’”).
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 Appendix D. 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.
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.
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3.2
CUDA C Runtime
The runtime is implemented in the cudart dynamic library which is typically
included in the application installation package. All its entry points are prefixed with
cuda.
As mentioned in Section 2.4, the CUDA programming model assumes a system
composed of a host and a device, each with their own separate memory.
Section 3.2.2 gives an overview of the runtime functions used to manage device
memory.
Section 3.2.3 illustrates the use of shared memory, introduced in Section 2.2, to
maximize performance.
Section 3.2.4 introduces page-locked host memory that is required to overlap kernel
execution with data transfers between host and device memory.
Section 3.2.5 describes the concepts and API used to enable asynchronous
concurrent execution at various levels in the system.
Section 3.2.6 shows how the programming model extends to a system with multiple
devices attached to the same host.
Section 3.2.8 describe how to properly check the errors generated by the runtime.
Section 3.2.9 mentions the runtime functions used to manage the CUDA C call
stack.
Section 3.2.10 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.
Section 3.2.11 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 Section G.1 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. 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 Section 3.2.6.2). 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.
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3.2.2
Device Memory
As mentioned in Section 2.4, the CUDA programming model assumes a system
composed of a host and a device, each with their own separate memory. Kernels
can only operate out of device memory, so the runtime provides functions to
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 Section 3.2.10.
Linear memory exists on the device in a 32-bit address space for devices of compute
capability 1.x and 40-bit address space of devices of higher compute capability, 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 cudaMemcpy(). In the vector addition code sample of
Section 2.1, 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
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int threadsPerBlock = 256;
int blocksPerGrid =
(N + threadsPerBlock – 1) / threadsPerBlock;
VecAdd<<>>(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 Section 5.3.2.1, 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 following code sample allocates a width×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×height×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);
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// 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.
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 Section B.2 shared memory is allocated using the __shared__
qualifier.
Shared memory is expected to be much faster than global memory as mentioned in
Section 2.2 and detailed in Section 5.3.2.3. 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
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one row of A and one column of B and computes the corresponding element of C
as illustrated in Figure 3-1. 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<<>>(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);
}
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B.width-1
// 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;
}
col
0
B.height
B
A
C
A.height
0
row
A.width
B.width
A.height-1
Figure 3-1. 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
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the block is responsible for computing one element of Csub. As illustrated in Figure
3-2, 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 submatrix 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.
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 (see Section B.1.1) are used to get and set elements and build any submatrix 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;
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}
// 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);
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<<>>(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);
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// 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();
// 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);
}
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col
0
blockRow
0
Csub
row
BLOCK_SIZE-1
BLOCK_SIZE
BLOCK_SIZE
A.height
C
BLOCK_SIZE
A
BLOCK_SIZE-1
BLOCK_SIZE
B
B.height
BLOCK_SIZE
blockCol
BLOCK_SIZE
A.width
B.width
Figure 3-2. 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;
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
Section 3.2.5;
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 Section 3.2.4.3;
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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 Section 3.2.4.2.
Page-locked host memory is a scarce resource however, so allocations in pagelocked 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 SDK sample comes with a detailed document on the pagelocked 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 Section 3.2.6 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
Section 3.2.7). 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
On devices of compute capability greater than 1.0, 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 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 Section 3.2.7.
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;
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There is no need to use streams (see Section 3.2.5.4) 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
Section 3.2.5) to avoid any potential read-after-write, write-after-read, or write-afterwrite hazards.
To be able to retrieve the device pointer to any mapped page-locked memory, pagelocked memory mapping must be enabled by calling cudaSetDeviceFlags()
with the cudaDeviceMapHost flag before any other CUDA calls 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 Section 3.2.6.1), which
is equal to 1 for devices that support mapped page-locked host memory.
Note that atomic functions (Section B.11) operating on mapped page-locked
memory are not atomic from the point of view of the host or other devices.
3.2.5
Asynchronous Concurrent Execution
3.2.5.1
Concurrent Execution between Host and Device
In order to facilitate concurrent execution between host and device, some function
calls are asynchronous: Control is returned to the host thread before the device has
completed the requested task. These are:
Kernel launches;
Memory copies between two addresses to the same device 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 asynchronous 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 never be used as a way to make production software run reliably.
When an application is run via cuda-gdb, the Visual Profiler, or the Parallel Nsight
CUDA Debugger, all launches are synchronous.
3.2.5.2
Overlap of Data Transfer and Kernel Execution
Some devices of compute capability 1.1 and higher can perform copies between
page-locked host memory and device memory concurrently with kernel execution.
Applications may query this capability by checking the asyncEngineCount device
property (see Section 3.2.6.1), which is greater than zero for devices that support it.
For devices of compute capability 1.x, this capability is only supported for memory
copies that do not involve CUDA arrays or 2D arrays allocated through
cudaMallocPitch() (see Section 3.2.2).
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3.2.5.3
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 Section 3.2.6.1), which is equal to 1 for
devices that support it.
The maximum number of kernel launches that a device can execute concurrently is
sixteen.
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.
3.2.5.4
Concurrent Data Transfers
Some devices of compute capability 2.x and higher can perform a copy from pagelocked host memory to device memory concurrently with a copy from device
memory to page-locked host memory.
Applications may query this capability by checking the asyncEngineCount device
property (see Section 3.2.6.1), which is equal to 2 for devices that support it.
3.2.5.5
Streams
Applications manage concurrency 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]);
}
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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.
Section 3.2.5.5.5 describes how the streams overlap in this example depending on
the capability of the 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]);
waits for all preceding commands in the given stream to
complete before destroying the stream and returning control to the host thread.
cudaStreamDestroy()
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.
3.2.5.5.3
Explicit Synchronization
There are various ways to explicitly synchronize streams with each other.
cudaDeviceSynchronize()
host threads have completed.
waits until all preceding commands in all streams of all
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.
cudaStreamSynchronize()
takes a stream and an event as parameters (see
Section 3.2.5.6 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.
cudaStreamWaitEvent()
provides applications with a way to know if all preceding
commands in a stream have completed.
cudaStreamQuery()
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.
3.2.5.5.4
Implicit Synchronization
Two commands from different streams cannot run concurrently if either one of the
following operations is issued in-between them by the host thread:
32
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 default stream,
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a switch between the L1/shared memory configurations described in
Section F.4.1.
For devices that support concurrent kernel execution, 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 (Section 3.2.5.2), concurrent
kernel execution (Section 3.2.5.3), and/or concurrent data transfers (Section 3.2.5.4).
For example, on devices that do not support concurrent data transfers, the two
streams of the code sample of Section 3.2.5.5.1 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]);
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 Section 3.2.5.5.1 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, 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
Section 3.2.5.5.4. If the code is rewritten as above, the kernel executions overlap
(assuming the device supports concurrent kernel execution) since the second kernel
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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 Section 3.2.5.5.4, which can represent only a small portion of the
total execution time of the kernel.
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 task 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 Section 3.2.5.6.1 can be used to time the code sample of
Section 3.2.5.5.1 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);
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 calls is
performed by the host thread.
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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
3.2.6.3
Stream and Event Behavior
A kernel launch or memory copy 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
cudaEventRecord()
different devices.
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will fail if the input event and input stream are associated to
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cudaEventElapsedTime()
different devices.
will fail if the two input events are associated to
and cudaEventQuery() will succeed even if the input
event is associated to a device that is different from the current device.
cudaEventSynchronize()
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.
cudaStreamWaitEvent()
Each device has its own default stream (see Section 3.2.5.5.2), 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 on Windows Vista/7 in TCC mode
(see Section 3.6), on Windows XP, or on Linux, 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.
A unified address space is used for both devices (see Section 3.2.7), so the same
pointer can be used to address memory from both devices as shown in the code
sample below.
cudaSetDevice(0);
float* p0;
size_t size = 1024 * sizeof(float);
cudaMalloc(&p0, size);
MyKernel<<<1000, 128>>>(p0);
cudaSetDevice(1);
cudaDeviceEnablePeerAccess(0, 0);
// Set device 0 as current
//
//
//
//
//
Allocate memory on device 0
Launch kernel on device 0
Set device 1 as current
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 Section 3.2.7), this is
done using the regular memory copy functions mentioned in Section 3.2.2.
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;
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cudaMalloc(&p1, size);
//
cudaSetDevice(0);
//
MyKernel<<<1000, 128>>>(p0);
//
cudaSetDevice(1);
//
cudaMemcpyPeer(p1, 1, p0, 0, size); //
MyKernel<<<1000, 128>>>(p1);
//
Allocate memory on device 1
Set device 0 as current
Launch kernel on device 0
Set device 1 as current
Copy p0 to p1
Launch kernel on device 1
A copy 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 asynchronous commands (see Section 3.2.5)
issued after the copy to either device can start.
Note that if peer-to-peer access is enabled between two devices via
cudaDeviceEnablePeerAccess() as described in Section 3.2.6.4, peer-to-peer
memory copy between these two devices no longer needs to be staged through the
host and is therefore faster.
3.2.7
Unified Virtual Address Space
For 64-bit applications on Windows Vista/7 in TCC mode (see Section 3.6), on
Windows XP, or on Linux, a single address space is used for the host and all the
devices of compute capability 2.0 and higher. This address space is used for all
allocations made in host memory via cudaHostAlloc()and in any of the device
memories via cudaMalloc*(). Which memory a pointer points to – host memory
or any of the device memories – can be determined from the value of the pointer
using cudaPointerGetAttributes(). As a consequence:
When copying from or to the memory of one of the devices for which the
unified address space is used, the cudaMemcpyKind parameter of
cudaMemcpy*() becomes useless and can be set to cudaMemcpyDefault;
Allocations via cudaHostAlloc() are automatically portable (see
Section 3.2.4.1) 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 Section 3.2.4.3).
Applications may query if the unified address space is used for a particular device by
checking that the unifiedAddressing device property (see Section 3.2.6.1) is
equal to 1.
3.2.8
Error Checking
All runtime functions return an error code, but for an asynchronous function (see
Section 3.2.5), 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
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cudaDeviceSynchronize() (or by using any other synchronization
mechanisms described in Section 3.2.5) 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().
3.2.9
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, Parallel Nsight) or an
unspecified launch error, otherwise.
3.2.10
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
Section 5.3.2.5.
3.2.10.1
Texture Memory
Texture memory is read from kernels using the device functions described in
Section B.8. The process of reading a texture is called a texture fetch. The first
parameter of a texture fetch specifies an object called a texture reference.
A texture reference defines which part of texture memory is fetched. As detailed in
Section 3.2.10.1.3, it must be bound through runtime functions to some region of
memory, called a texture, before it can be used by a kernel. Several distinct texture
references might be bound to the same texture or to textures that overlap in
memory.
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A texture reference has several attributes. One of them is 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 threedimensional array using three texture coordinates. Elements of the array are called
texels, short for “texture elements.” The type of a texel is restricted to the basic
integer and single-precision floating-point types and any of the 1-, 2-, and 4component vector types defined in Section B.3.1
Other attributes define the input and output data types of the texture fetch, as well
as how the input coordinates are interpreted and what processing should be done.
A texture can be any region of linear memory or a CUDA array (described in
Section 3.2.10.2.3).
Table F-2 lists the maximum texture width, height, and depth depending on the
compute capability of the device.
Textures can also be layered as described in Section 3.2.10.1.5.
3.2.10.1.1
Texture Reference Declaration
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 texRef;
where:
DataType specifies the type of data that is returned when fetching the texture;
Type 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 Section B.3.1;
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 is equal to cudaReadModeNormalizedFloat or
cudaReadModeElementType; if it is cudaReadModeNormalizedFloat
and Type is a 16-bit or 8-bit integer type, the value 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; ReadMode 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.
3.2.10.1.2
Runtime Texture Reference Attributes
The other attributes of a texture reference are mutable and can be changed at
runtime through the host runtime. They specify whether texture coordinates are
normalized or not, the addressing mode, and texture filtering, as detailed below.
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By default, textures are referenced (by the functions of Section B.8) using floatingpoint 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 6432 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 6432
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.
It is valid to call the device functions of Section B.8 with coordinates that are be out
of range. The addressing mode defines what happens if that case. The default
addressing mode is to clamp the coordinates to the valid range: [0, N) for nonnormalized 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 warp 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.
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 onedimensional textures, bilinear interpolation for two-dimensional textures, and
trilinear interpolation for three-dimensional textures.
Appendix E gives more details on texture fetching.
3.2.10.1.3
Texture Binding
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
enum cudaTextureFilterMode
enum cudaTextureAddressMode
struct cudaChannelFormatDesc
}
normalized;
filterMode;
addressMode[3];
channelDesc;
normalized specifies whether texture coordinates are normalized or not, as
described in Section 3.2.10.1.2;
filterMode specifies the filtering mode, that is how the value returned when
fetching the texture is computed based on the input texture coordinates;
filterMode 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
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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;
addressMode specifies the addressing mode, as described in
Section 3.2.10.1.2; addressMode is 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;
channelDesc describes the format of the value that is returned when fetching
the texture; 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.
normalized, addressMode, and filterMode may be directly modified in host
code.
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.
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 texture reference to linear memory pointed to by
devPtr:
Using the low-level API:
texture texRef;
textureReference* texRefPtr;
cudaGetTextureReference(&texRefPtr, "texRef");
cudaChannelFormatDesc channelDesc =
cudaCreateChannelDesc();
size_t offset;
cudaBindTexture2D(&offset, texRefPtr, devPtr, &channelDesc,
width, height, pitch);
Using the high-level API:
texture texRef;
cudaChannelFormatDesc channelDesc =
cudaCreateChannelDesc();
size_t offset;
cudaBindTexture2D(&offset, texRef, devPtr, channelDesc,
width, height, pitch);
The following code samples bind a texture reference to a CUDA array cuArray:
Using the low-level API:
texture texRef;
textureReference* texRefPtr;
cudaGetTextureReference(&texRefPtr, "texRef");
cudaChannelFormatDesc channelDesc;
cudaGetChannelDesc(&channelDesc, cuArray);
cudaBindTextureToArray(texRef, cuArray, &channelDesc);
Using the high-level API:
texture 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.
The following code sample applies some simple transformation kernel to a texture.
// 2D float texture
texture 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,
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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 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<<>>(output, width, height,
angle);
// Free device memory
cudaFreeArray(cuArray);
cudaFree(output);
return 0;
}
3.2.10.1.4
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.
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3.2.10.1.5
Layered Textures
A one-dimensional or two-dimensional layered texture (also know 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 floatingpoint 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 bound to a CUDA array created 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 Sections B.8.5
and B.8.6. Texture filtering (see Appendix E) is done only within a layer, not across
layers.
Layered textures are only supported on devices of compute capability 2.0 and
higher.
3.2.10.1.6
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 3-1.
Table 3-1.
Cubemap Fetch
face
|x| > |y| and |x| > |z|
|y| > |x| and |y| > |z|
|z| > |x| and |z| > |y|
m
s
t
x≥0
0
x
-z
-y
x<0
1
-x
z
-y
y≥0
2
y
x
z
y<0
3
-y
x
-z
z≥0
4
z
x
-y
z<0
5
-z
-x
-y
A layered texture can only be bound to a CUDA array created by calling
cudaMalloc3DArray() with the cudaArrayCubemap flag.
Cubemap textures are fetched using the device function described in Sections B.8.7.
Cubemap textures are only supported on devices of compute capability 2.0 and
higher.
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3.2.10.1.7
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 floatingpoint 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 bound to a CUDA array created by calling
cudaMalloc3DArray() with the cudaArrayLayered and
cudaArrayCubemap flags.
Cubemap layered textures are fetched using the device function described in
Sections B.8.8. Texture filtering (see Appendix E) is done only within a layer, not
across layers.
Cubemap layered textures are only supported on devices of compute capability 2.0
and higher.
3.2.10.1.8
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 Section B.8.9). 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).
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 F-2 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.
3.2.10.2
Surface Memory
For devices of compute capability 2.0 and higher, a CUDA array (described in
Section 3.2.10.2.3), created with the cudaArraySurfaceLoadStore flag, can be
read and written via a surface reference using the functions described in Section B.9.
Table F-2 lists the maximum surface width, height, and depth depending on the
compute capability of the device.
3.2.10.2.1
Surface Reference Declaration
A surface reference is declared at file scope as a variable of type surface:
surface 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.
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A surface reference can only be declared as a static global variable and cannot be
passed as an argument to a function.
3.2.10.2.2
Surface Binding
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 surfRef;
surfaceReference* surfRefPtr;
cudaGetSurfaceReference(&surfRefPtr, "surfRef");
cudaChannelFormatDesc channelDesc;
cudaGetChannelDesc(&channelDesc, cuArray);
cudaBindSurfaceToArray(surfRef, cuArray, &channelDesc);
Using the high-level API:
surface 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).
The following code sample applies some simple transformation kernel to a texture.
// 2D surfaces
surface inputSurfRef;
surface 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);
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}
}
// 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<<>>(width, height);
// Free device memory
cudaFreeArray(cuInputArray);
cudaFreeArray(cuOutputArray);
return 0;
}
3.2.10.2.3
Cubemap Surfaces
Cubemap surfaces are accessed using surfCubemapread() and
surfCubemapwrite() (Sections B.9.11 and B.9.12) 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 3-1.
3.2.10.2.4
Cubemap Layered Surfaces
Cubemap layered surfaces are accessed using surfCubemapLayeredread() and
surfCubemapLayeredwrite() (Sections B.9.13 and B.9.14) as a twodimensional 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 3-1, so
index ((2 * 6) + 3), for example, accesses the fourth face of the third cubemap.
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3.2.10.3
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
readable by kernels through texture fetching and may only be bound to texture
references with the same number of packed components.
3.2.10.4
Read/Write Coherency
The texture and surface memory is cached (see Section 5.3.2.5) 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.
3.2.11
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 Sections 3.2.11.1 and 3.2.11.2. 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().
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.
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 or Direct3D while it is mapped to CUDA
produces undefined results.
Sections 3.2.11.1 and 3.2.11.2 give specifics for each graphics API and some code
samples.
Section 3.2.11.3 gives specifics for when the system is in SLI mode.
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3.2.11.1
OpenGL Interoperability
Interoperability with OpenGL requires that the CUDA device be specified by
cudaGLSetGLDevice() before any other runtime calls. Note that
cudaSetDevice()and cudaGLSetGLDevice() are mutually exclusive.
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.
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
cudaGLSetGLDevice(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();
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...
}
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<<>>(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();
}
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;
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// 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.
3.2.11.2
Direct3D Interoperability
Direct3D interoperability is supported for Direct3D 9, Direct3D 10, and
Direct3D 11.
A CUDA context may interoperate with only one Direct3D device at a time and the
CUDA context and Direct3D device must be created on the same GPU. In addition
the following considerations must be taken when creating the device: Direct3D 9
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.
Interoperability with Direct3D requires that the Direct3D device be specified by
cudaD3D9SetDirect3DDevice(), cudaD3D10SetDirect3DDevice() and
cudaD3D11SetDirect3DDevice(), before any other runtime calls.
cudaD3D9GetDevice(), cudaD3D10GetDevice(), and
cudaD3D11GetDevice() can be used to retrieve the CUDA device associated to
some adapter.
A set of calls is also available to allow the creation of CUDA contexts with
interoperability with Direct3D devices that use NVIDIA SLI in AFR (Alternate
Frame Rendering) mode: cudaD3D[9|10|11]GetDevices(). A call to
cudaD3D[9|10|11]GetDevices()can be used to obtain a list of CUDA device
handles that can be passed as the (optional) last parameter to
cudaD3D[9|10|11]SetDirect3DDevice().
The application has the choice to either create multiple CPU threads, each using a
different CUDA context, or a single CPU thread using multiple CUDA context. If
using separate CPU threads for each GPU each of the CUDA contexts would be
created by the CUDA runtime by calling in a separate CPU thread
cudaD3D[9|10|11]SetDirect3DDevice()using one of the CUDA device
handles returned by cudaD3D[9|10|11]GetDevices().
If using a single CPU thread the CUDA contexts would have to be created using the
CUDA driver API context creation functions for interoperability with Direct3D
devices that use NVIDIA SLI (cuD3D[9|10|11]CtxCreateOnDevice()). The
application relies on the interoperability between CUDA driver and runtime APIs
(Section G.4), which allows it to call cuCtxPushCurrent() and
cuCtxPopCurrent()to change the CUDA context active at a given time.
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(),
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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.
Direct3D 9 Version:
IDirect3D9* D3D;
IDirect3DDevice9* device;
struct CUSTOMVERTEX {
FLOAT x, y, z;
DWORD color;
};
IDirect3DVertexBuffer9* positionsVB;
struct cudaGraphicsResource* positionsVB_CUDA;
int main()
{
// Initialize Direct3D
D3D = Direct3DCreate9(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);
int dev;
if (cudaD3D9GetDevice(&dev, adapterId.DeviceName)
== cudaSuccess)
break;
}
// Create device
...
D3D->CreateDevice(adapter, D3DDEVTYPE_HAL, hWnd,
D3DCREATE_HARDWARE_VERTEXPROCESSING,
¶ms, &device);
// Register device with CUDA
cudaD3D9SetDirect3DDevice(device);
// 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<<>>(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));
}
Direct3D 10 Version:
ID3D10Device* device;
struct CUSTOMVERTEX {
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FLOAT x, y, z;
DWORD color;
};
ID3D10Buffer* positionsVB;
struct cudaGraphicsResource* positionsVB_CUDA;
int main()
{
// 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;
int dev;
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();
// Register device with CUDA
cudaD3D10SetDirect3DDevice(device);
// 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<<>>(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));
}
Direct3D 11 Version:
ID3D11Device* device;
struct CUSTOMVERTEX {
FLOAT x, y, z;
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DWORD color;
};
ID3D11Buffer* positionsVB;
struct cudaGraphicsResource* positionsVB_CUDA;
int main()
{
// 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;
int dev;
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();
// Register device with CUDA
cudaD3D11SetDirect3DDevice(device);
// 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<<>>(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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3.2.11.3
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 have to create multiple CUDA contexts, one for each GPU in
the SLI configuration and deal with the fact that a different GPU is used for
rendering by the Direct3D or OpenGL device at every frame. The application can
use the cudaD3D[9|10|11]GetDevices() for 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 map Direct3D or
OpenGL resources to the CUDA context corresponding to the CUDA device
returned by cudaD3D[9|10|11]GetDevices() or cudaGLGetDevices()
when the deviceList parameter is set to
CU_D3D10_DEVICE_LIST_CURRENT_FRAME or
cudaGLDeviceListCurrentFrame.
See Sections 3.2.11.2 and 3.2.11.1 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 Section 2.5) 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 3-3.
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 mixing and matching versions is not supported;
specifically:
All applications, plug-ins, and libraries on a system must use the same version of
the CUDA driver API, since only one version of the CUDA device driver can
be installed on a system.
All plug-ins and libraries used by an application must use the same version of
the runtime.
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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, …).
Apps,
Libs &
Plug-ins
Apps,
Libs &
Plug-ins
1.0
Driver
1.1
Driver
Compatible
Apps,
Libs &
Plug-ins ...
2.0
Driver
...
Incompatible
Figure 3-3. 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 mode. cudaSetValidDevices() can be used to set a device from a
prioritized list of devices.
Applications may query the compute mode of a device by checking the
computeMode device property (see Section 3.2.6.1).
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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:
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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Hardware Implementation
The CUDA 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 Section 4.1. 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 Section 4.2. Unlike CPU cores they are issued in order
however and there is no branch prediction and no speculative execution.
Sections 4.1 and 4.2 describe the architecture features of the streaming
multiprocessor that are common to all devices. Sections F.3.1, F.4.1, and F.5.1
provide the specifics for devices of compute capabilities 1.x, 2.x, and 3.0,
respectively.
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
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.
Section 2.2 describes how thread IDs relate to thread indices in the block.
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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 serially executes each
branch path taken, disabling threads that are not on that path, and when all paths
complete, the threads converge back to the same execution 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.
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 Sections F.3.2, F.3.3, F.4.2, F.4.3, F.5.2, and F.5.3) and
which thread performs the final write is undefined.
If an atomic instruction (see Section B.11) 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
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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 F.
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 Wblock in a block is as follows:
Wblock ceil (
T
,1)
Wsize
T is the number of threads per block,
Wsize is the warp size, which is equal to 32,
ceil(x, y) is equal to x rounded up to the nearest multiple of y.
The total number of registers Rblock allocated for a block is as follows:
For devices of compute capability 1.x:
Rblock ceil (ceil (Wblock , GW ) Wsize Rk , GT )
For devices of compute capability 2.x:
Rblock ceil ( Rk Wsize , GR ) Wblock
GW is the warp allocation granularity, equal to 2 (compute capability 1.x only),
Rk is the number of registers used by the kernel,
GR is the register allocation granularity, which is equal to
256 for devices of compute capability 1.0 and 1.1,
512 for devices of compute capability 1.2 and 1.3,
64 for devices of compute capability 2.x,
256 for devices of compute capability 3.0.
The total amount of shared memory Sblock in bytes allocated for a block is as follows:
S block ceil (S k , GS )
Sk is the amount of shared memory used by the kernel in bytes,
GS is the shared memory allocation granularity, which is equal to
512 for devices of compute capability 1.x,
128 for devices of compute capability 2.x,
256 for devices of compute capability 3.0.
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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 Section 3.2.5. It should assign to each
processor the type of work it does best: serial workloads to the host; parallel
workloads to the devices.
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
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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.
For devices of compute capability 1.x, only one kernel can execute on a device at
one time, so the kernel should be launched with at least as many thread blocks as
there are multiprocessors in the device.
For devices of compute capability 2.x and higher, 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
Section 3.2.5.
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 Section 4.2, 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 Section 5.4.1 for the throughputs of various arithmetic
instructions); assuming maximum throughput for all instructions, it is:
L/4 (rounded up to nearest integer) for devices of compute capability 1.x since
a multiprocessor issues one instruction per warp over four clock cycles, as
mentioned in Section F.3.1,
L for devices of compute capability 2.0 since a multiprocessor issues one
instruction per warp over two clock cycles for two warps at a time, as
mentioned in Section F.4.1,
2L for devices of compute capability 2.1 since a multiprocessor issues a pair of
instructions per warp over two clock cycles for two warps at a time, as
mentioned in Section F.4.1,
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8L for devices of compute capability 3.0 since a multiprocessor issues a pair of
instructions per warp over one clock cycle for four warps at a time, as
mentioned in Section F.5.1.
For devices of compute capability 2.0, the two instructions issued every other cycle
are for two different warps. For devices of compute capability 2.1, the four
instructions issued every other cycle are two pairs for two different warps, each pair
being for the same warp.
For devices of compute capability 3.0, 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 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 22 clock cycles for devices of
compute capability 1.x and 2.x and about 11 clock cycles for devices of compute
capability 3.0, which translates to 6 warps for devices of compute capability 1.x and
22 warps for devices of compute capability 2.x and higher (still assuming that warps
execute instructions with maximum throughput, otherwise fewer warps are needed).
For devices of compute capability 2.1 and higher, 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: 400
to 800 clock cycles. 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). If this ratio is 15, for example, then to hide latencies of about 600 clock
cycles, about 10 warps are required for devices of compute capability 1.x and about
40 for devices of compute capability 2.x and higher (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 (Section B.5) or synchronization point (Section B.6). 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 (Section B.18), the memory
resources of the multiprocessor, and the resource requirements of the kernel as
described in Section 4.2. To assist programmers in choosing thread block size based
on register and shared memory requirements, the CUDA Software Development
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Kit provides a spreadsheet, called the CUDA Occupancy Calculator, where
occupancy is defined as the ratio of the number of resident warps to the maximum
number of resident warps (given in Appendix F for various compute capabilities).
Register, local, shared, and constant memory usages 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, the amount of dynamically allocated
shared memory, and for devices of compute capability 1.x, the amount of shared
memory used to pass the kernel’s arguments (see Section B.1.4).
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 1.2, if a
kernel uses 16 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 2x512x16 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
2x512x17 registers, which are more registers than are available on the
multiprocessor. Therefore, the compiler attempts to minimize register usage while
keeping register spilling (see Section 5.3.2.2) and the number of instructions to a
minimum. Register usage can be controlled using the -maxrregcount compiler
option or launch bounds as described in Section B.19.
Each double variable (on devices that supports native double precision, i.e. devices
of compute capability 1.2 and higher) and each long long variable uses two
registers. However, devices of compute capability 1.2 and higher have at least twice
as many registers per multiprocessor as devices with lower compute capability.
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.
5.3
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 Section 5.3.1, 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/L2
caches available on devices of compute capability 2.x and higher, texture cache and
constant cache available on all devices).
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Shared memory is equivalent to a user-managed cache: The application explicitly
allocates and accesses it. As illustrated in Section 3.2.3, 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 datadependent), a traditional hardware-managed cache is more appropriate to exploit
data locality. As mentioned in Section F.4.1, for devices of compute capability 2.x
and higher, 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 Sections 5.3.2.1,
5.3.2.3, 5.3.2.4, and 5.3.2.5. 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
Section 3.2.4.
In addition, when using mapped page-locked memory (Section 3.2.4.3), 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 Section 5.3.2.1). Assuming that
they 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.
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On integrated systems where device memory and host memory are physically the
same, any copy between host and device memory is superfluous and mapped pagelocked memory should be used instead. Applications may query a device is
integrated by checking that the integrated device property (see Section 3.2.6.1) 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.
5.3.2.1
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. For devices of compute
capability 1.0 and 1.1, the requirements on the distribution of the addresses across
the threads to get any coalescing at all are very strict. They are much more relaxed
for devices of higher compute capabilities. For devices of compute capability 2.x
and higher, the memory transactions are cached, so data locality is exploited to
reduce impact on throughput. Sections F.3.2, F.4.2, and F.5.2 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 Sections F.3.2 and F.4.2,
Using data types that meet the size and alignment requirement detailed in
Section 5.3.2.1.1,
Padding data in some cases, for example, when accessing a two-dimensional
array as described in Section 5.3.2.1.2.
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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
Section B.3.1 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.
5.3.2.1.2
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 BaseAddress of type type* (where type meets the
requirement described in Section 5.3.2.1.1):
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 (or only half the warp size for
devices of compute capability 1.x).
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
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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.
5.3.2.2
Local Memory
Local memory accesses only occur for some automatic variables as mentioned in
Section B.2. 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 Section 5.3.2.1. 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 devices of compute capability 2.x and higher, local memory accesses are always
cached in L1 and L2 in the same way as global memory accesses (see Section F.4.2).
5.3.2.3
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.
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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 Sections F.3.3, F.4.3, and F.5.3 for
devices of compute capability 1.x, 2.x, and 3.0, respectively.
5.3.2.4
Constant Memory
The constant memory space resides in device memory and is cached in the constant
cache mentioned in Sections F.3.1 and F.4.1.
For devices of compute capability 1.x, a constant memory request for a warp is first
split into two requests, one for each half-warp, that are issued independently.
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.
5.3.2.5
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. 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 respect to get good performance (see Sections 5.3.2.1 and
5.3.2.4), higher bandwidth can be achieved providing that there is locality in the
texture fetches or surface reads (this is less likely for devices of compute
capability 2.x and higher given that global memory reads are cached on these
devices);
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 Section 3.2.10.1.1).
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
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Section C.2), single-precision instead of double-precision, or flushing
denormalized numbers to zero;
Minimize divergent warps caused by control flow instructions as detailed in
Section 5.4.2;
Reduce the number of instructions, for example, by optimizing out
synchronization points whenever possible as described in Section 5.4.3 or by
using restricted pointers as described in Section B.2.4.
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.
Therefore, if T is the number of operations per clock cycle, the instruction
throughput is one instruction every 32/T clock cycles.
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 5-1 gives the throughputs of the arithmetic instructions that are natively
supported in hardware for devices of various compute capabilities.
Table 5-1.
Throughput of Native Arithmetic Instructions
(Operations per Clock Cycle per Multiprocessor)
Compute Capability
74
1.0
1.1
1.2
1.3
2.0
2.1
3.0
32-bit floating-point
add, multiply, multiplyadd
8
8
32
48
192
64-bit floating-point
add, multiply, multiplyadd
1
1
16(*)
4
8
32-bit integer add
10
10
32
48
192
32-bit integer compare
10
10
32
48
160
32-bit integer shift
8
8
16
16
32
Logical operations
8
8
32
48
160
32-bit integer
multiply, multiply-add,
sum of absolute
difference
Multiple
instructions
Multiple
instructions
16
16
32
24-bit integer multiply
(__[u]mul24)
8
8
Multiple
instructions
Multiple
instructions
Multiple
instructions
32-bit floating-point
reciprocal, reciprocal
square root,
base-2 logarithm
(__log2f),
base-2 exponential
(exp2f),
2
2
4
8
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sine (__sinf), cosine
(__cosf)
Type conversions from
8-bit and 16-bit integer
to 32-bit integer
8
8
16
16
128
Type conversions from
and to 64-bit types
Multiple
instructions
1
16(*)
4
8
8
8
16
16
32
All other type
conversions
(*) Throughput is lower for GeForce GPUs
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.
Single-Precision Floating-Point Addition and Multiplication Intrinsics
__fadd_r[d,u], __fmul_r[d,u], and __fmaf_r[n,z,d,u] (see
Section C.2.1) compile to tens of instructions for devices of compute capability 1.x,
but map to a single native instruction for devices of compute capability 2.x and
higher.
Single-Precision Floating-Point Division
__fdividef(x, y) (see Section C.2.1) 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
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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 math_functions.h 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 48039.0f for the singleprecision 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 Section 5.3.2.2). At present, 28 bytes of local
memory are used by single-precision functions, and 44 bytes are used by doubleprecision 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.
Integer Arithmetic
On devices of compute capability 1.x, 32-bit integer multiplication is implemented
using multiple instructions as it is not natively supported. 24-bit integer
multiplication is natively supported however via the __[u]mul24 intrinsic. Using
__[u]mul24 instead of the 32-bit multiplication operator whenever possible
usually improves performance for instruction bound kernels. It can have the
opposite effect however in cases where the use of __[u]mul24 inhibits compiler
optimizations.
On devices of compute capability 2.x and beyond, 32-bit integer multiplication is
natively supported, but 24-bit integer multiplication is not. __[u]mul24 is
therefore implemented using multiple instructions and should not be used.
Integer division and modulo operation are costly: tens of instructions on devices of
compute capability 1.x, below 20 instructions on devices of compute capability 2.x
and higher. 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, __brevll, __popc, and __popcll compile to tens of instructions for
devices of compute capability 1.x, but __brev and __popc map to a single
instruction for devices of compute capability 2.x and higher and __brevll and
__popcll to just a few.
__clz, __clzll, __ffs, and __ffsll compile to fewer instructions for devices
of compute capability 2.x and higher than for devices of compute capability 1.x.
Type Conversion
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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. When all the different execution paths have completed, the
threads converge back to the same execution path.
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 Section 4.1. A trivial example is when the
controlling condition only depends on (threadIdx / warpSize) where
warpSize is 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 if or switch
statements 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 Section B.20).
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.
The compiler replaces a branch instruction with predicated instructions only if the
number of instructions controlled by the branch condition is less or equal to a
certain threshold: If the compiler determines that the condition is likely to produce
many divergent warps, this threshold is 7, otherwise it is 4.
5.4.3
Synchronization Instruction
Throughput for __syncthreads() is 8 operations per clock cycle for devices of
compute capability 1.x, 16 operations per clock cycle for devices of compute
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capability 2.x, and 128 operations per clock cycle for devices of compute
capability 3.0.
Note that __syncthreads() can impact performance by forcing the
multiprocessor to idle as detailed in Section 5.2.3.
Because a warp executes one common instruction at a time, threads within a warp
are implicitly synchronized and this can sometimes be used to omit
__syncthreads() for better performance.
In the following code sample, for example, there is no need to call
__syncthreads() after each of the additions performed within the body of the
“if (tid < 32) { }“ statement since they operate within a single warp (this is
assuming the size of a warp is 32).
Simply removing the __syncthreads() is not enough however; smem must also
be declared as volatile as described in Section D.2.1.2.
__device__ void sum(float* g_idata, float* g_odata)
{
unsigned int tid = threadIdx.x;
extern __shared__ float s_data[];
// Assign initial value
s_data[tid] = g_idata[...];
__syncthreads();
//
//
//
//
if
Perform sum in shared memory.
This code sample assumes that the block size is 256
(see the reduction sample in the GPU Computing SDK
for a complete and general implementation
(tid < 128)
s_data[tid] += s_data[tid + 128];
__syncthreads();
if (tid < 64)
s_data[tid] += s_data[tid + 64];
__syncthreads();
if (tid < 32) {
// No __syncthreads() necessary after each of the
// following lines (as long as we access the data via
// a pointer declared as volatile) because the 32 threads
// in each warp execute in lock-step with each other
volatile float* s_ptr = s_data;
s_ptr[tid] += s_ptr[tid + 32];
s_ptr[tid] += s_ptr[tid + 16];
s_ptr[tid] += s_ptr[tid + 8];
s_ptr[tid] += s_ptr[tid + 4];
s_ptr[tid] += s_ptr[tid + 2];
s_ptr[tid] += s_ptr[tid + 1];
}
// Write result for this thread block to global memory
if (tid == 0)
g_odata[blockIdx.x] = s_data[0];
}
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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 Type Qualifiers
Function type qualifiers specify 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__ qualifier declares a function that is:
Executed on the device
Callable from the device only.
B.1.2
__global__
The __global__ qualifier declares a function as being a kernel. Such a function is:
Executed on the device,
Callable from the host only.
__global__ functions must have void return type.
Any call to a __global__ function must specify its execution configuration as
described in Section B.18.
A call to a __global__ function is asynchronous, meaning it returns before the
device has completed its execution.
B.1.3
__host__
The __host__ qualifier declares a function that is:
Executed on the host,
Callable from the host only.
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It is equivalent to declare a function with only the __host__ qualifier or to declare
it without any of the __host__, __device__, or __global__ qualifier; in either
case the function is compiled for the host only.
The __global__ and __host__ qualifiers cannot be used together.
The __device__ and __host__ qualifiers 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 Section 3.1.4 can be used to differentiate
code paths between host and device:
__host__ __device__ func()
{
#if __CUDA_ARCH__ == 100
// Device code path for compute capability 1.0
#elif __CUDA_ARCH__ == 200
// Device code path for compute capability 2.0
#elif __CUDA_ARCH__ == 300
// Device code path for compute capability 3.0
#elif !defined(__CUDA_ARCH__)
// Host code path
#endif
}
B.1.4
__noinline__ and __forceinline__
When compiling code for devices of compute capability 1.x, a __device__
function is always inlined by default. When compiling code for devices of compute
capability 2.x and higher, a __device__ function is only inlined when deemed
appropriate by the compiler.
The __noinline__ function qualifier can be used as a hint for the compiler not to
inline the function if possible. The function body must still be in the same file where
it is called. For devices of compute capability 1.x, the compiler will not honor the
__noinline__ qualifier for functions with pointer parameters and for functions
with large parameter lists. For devices of compute capability 2.x and higher, the
compiler will always honor the __noinline__ qualifier.
The __forceinline__ function qualifier can be used to force the compiler to
inline the function.
B.2
Variable Type Qualifiers
Variable type qualifiers specify the memory location on the device of a variable.
An automatic variable declared in device code without any of the __device__,
__shared__ and __constant__ qualifiers described in this section 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
Section 5.3.2.2.
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B.2.1
C Language Extensions
__device__
The __device__ qualifier declares a variable that resides on the device.
At most one of the other type qualifiers defined in the next three sections may be
used together with __device__ to further specify which memory space the
variable belongs to. If none of them is present, the variable:
Resides in global memory space,
Has the lifetime of an application,
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__ qualifier, optionally used together with __device__,
declares a variable that:
Resides in constant memory space,
Has the lifetime of an application,
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__ qualifier, 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,
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 Section B.18). All variables
declared in this fashion, start at the same address in memory, so that the layout 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;
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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 B-1.
B.2.4
__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 reordering to common sub-expression elimination.
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);
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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.
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
char1, uchar1, char2, uchar2, char3, uchar3,
char4, uchar4, short1, ushort1, short2, ushort2,
short3, ushort3, short4, ushort4, int1, uint1, int2,
uint2, int3, uint3, int4, uint4, long1, ulong1,
long2, ulong2, long3, ulong3, long4, ulong4,
longlong1, ulonglong1, longlong2, ulonglong2,
float1, float2, float3, float4, double1, double2
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_; for example,
int2 make_int2(int x, int y);
which creates a vector of type int2 with value (x, y).
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In host code, the alignment requirement of a vector type is equal to the alignment
requirement of its base type. This is not always the case in device code as detailed in
Table B-1.
Table B-1. Alignment Requirements in Device Code
B.3.2
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
longlong1, ulonglong1
8
longlong2, ulonglong2
16
float1
4
float2
8
float3
4
float4
16
double1
8
double2
16
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.
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B.4.1
C Language Extensions
gridDim
This variable is of type dim3 (see Section B.3.2) and contains the dimensions of the
grid.
B.4.2
blockIdx
This variable is of type uint3 (see Section B.3.1) and contains the block index
within the grid.
B.4.3
blockDim
This variable is of type dim3 (see Section B.3.2) and contains the dimensions of the
block.
B.4.4
threadIdx
This variable is of type uint3 (see Section B.3.1) and contains the thread index
within the block.
B.4.5
warpSize
This variable is of type int and contains the warp size in threads (see Section 4.1
for the definition of a warp).
B.5
Memory Fence Functions
void __threadfence_block();
waits until all global and shared memory accesses made by the calling thread prior to
__threadfence_block() are visible to all threads in the thread block.
void __threadfence();
waits until all global and shared memory accesses made by the calling thread prior to
__threadfence() are visible to:
All threads in the thread block for shared memory accesses,
All threads in the device for global memory accesses.
void __threadfence_system();
waits until all global and shared memory accesses made by the calling thread prior to
__threadfence_system() are visible to:
All threads in the thread block for shared memory accesses,
All threads in the device for global memory accesses,
Host threads for page-locked host memory accesses (see Section 3.2.4.3).
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C Language Extensions
__threadfence_system() is only supported by devices of compute
capability 2.x and higher.
In general, when a thread issues a series of writes to memory in a particular order,
other threads may see the effects of these memory writes in a different order.
__threadfence_block(), __threadfence(), and
__threadfence_system() can be used to enforce some ordering.
One 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 Section B.11 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.
__device__ unsigned int count = 0;
__shared__ bool isLastBlockDone;
__global__ void sum(const float* array, unsigned int N,
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
result[blockIdx.x] = partialSum;
// Thread 0 makes sure its result is visible to
// all other threads
__threadfence();
// Thread 0 of each block signals that it is done
unsigned int value = atomicInc(&count, gridDim.x);
// Thread 0 of each block 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]
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C Language Extensions
float totalSum = calculateTotalSum(result);
if (threadIdx.x == 0) {
// Thread 0 of last block stores total sum
// to global memory and resets count so that
// next kernel call works properly
result[0] = totalSum;
count = 0;
}
}
}
B.6
Synchronization Functions
void __syncthreads();
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.
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.
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C Language Extensions
Appendix C provides accuracy information for some of these functions when
relevant.
B.8
Texture Functions
For texture functions, a combination of the texture reference’s immutable (i.e.
compile-time) and mutable (i.e. runtime) attributes determine how the texture
coordinates are interpreted, what processing occurs during the texture fetch, and the
return value delivered by the texture fetch. Immutable attributes are described in
Section 3.2.10.1.1. Mutable attributes are described in Section 3.2.10.1.2. Texture
fetching is described in Appendix E.
B.8.1
tex1Dfetch()
template
Type tex1Dfetch(
texture texRef,
int x);
float tex1Dfetch(
texture texRef,
int x);
float tex1Dfetch(
texture texRef,
int x);
float tex1Dfetch(
texture texRef,
int x);
float tex1Dfetch(
texture texRef,
int x);
fetch the region of linear memory bound to texture reference texRef using integer
texture coordinate x. tex1Dfetch() only works with non-normalized coordinates
(Section 3.2.10.1.2), so only the border and clamp addressing modes are supported.
are supported. It does not perfrom 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 texRef,
int x);
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fetches the region of linear memory bound to texture reference texRef using
texture coordinate x.
B.8.2
tex1D()
template
Type tex1D(texture texRef,
float x);
fetches the CUDA array bound to the one-dimensional texture reference texRef
using texture coordinate x.
B.8.3
tex2D()
template
Type tex2D(texture texRef,
float x, float y);
fetches the CUDA array or the region of linear memory bound to the twodimensional texture reference texRef using texture coordinates x and y.
B.8.4
tex3D()
template
Type tex3D(texture texRef,
float x, float y, float z);
fetches the CUDA array bound to the three-dimensional texture reference texRef
using texture coordinates x, y, and z.
B.8.5
tex1DLayered()
template
Type tex1DLayered(
texture texRef,
float x, int layer);
fetches the CUDA array bound to the one-dimensional layered texture reference
texRef using texture coordinate x and index layer, as described in Section
3.2.10.1.5.
B.8.6
tex2DLayered()
template
Type tex2DLayered(
texture texRef,
float x, float y, int layer);
fetches the CUDA array bound to the two-dimensional layered texture reference
texRef using texture coordinates x and y, and index layer, as described in
Section 3.2.10.1.5.
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B.8.7
C Language Extensions
texCubemap()
template
Type texCubemap(
texture texRef,
float x, float y, float z);
fetches the CUDA array bound to the cubemap texture reference texRef using
texture coordinates x, y, and z, as described in Section 3.2.10.1.6.
B.8.8
texCubemapLayered()
template
Type texCubemapLayered(
texture texRef,
float x, float y, float z, int layer);
fetches the CUDA array bound to the cubemap layered texture reference texRef
using texture coordinates x, y, and z, and index layer, as described in
Section 3.2.10.1.7.
B.8.9
tex2Dgather()
template
Type tex2Dgather(
texture texRef,
float x, float y, int comp = 0);
fetches the CUDA array bound to the cubemap texture reference texRef using
texture coordinates x and y, as described in Section 3.2.10.1.8.
B.9
Surface Functions
Surface functions are only supported by devices of compute capability 2.0 and
higher.
Surface reference declaration is described in Section 3.2.10.2.1 and surface binding
in Section 3.2.10.2.2.
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
surf1Dread()
template
Type surf1Dread(surface surfRef,
int x,
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boundaryMode = cudaBoundaryModeTrap);
template
void surf1Dread(Type data,
surface surfRef,
int x,
boundaryMode = cudaBoundaryModeTrap);
reads the CUDA array bound to the one-dimensional surface reference surfRef
using coordinate x.
B.9.2
surf1Dwrite()
template
void surf1Dwrite(Type data,
surface surfRef,
int x,
boundaryMode = cudaBoundaryModeTrap);
writes value data to the CUDA array bound to the one-dimensional surface
reference surfRef at coordinate x.
B.9.3
surf2Dread()
template
Type surf2Dread(surface surfRef,
int x, int y,
boundaryMode = cudaBoundaryModeTrap);
template
void surf2Dread(Type* data,
surface 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.9.4
surf2Dwrite()
template
void surf2Dwrite(Type data,
surface surfRef,
int x, int y,
boundaryMode = cudaBoundaryModeTrap);
writes value data to the CUDA array bound to the two-dimensional surface
reference surfRef at coordinate x and y.
B.9.5
surf3Dread()
template
Type surf3Dread(surface surfRef,
int x, int y, int z,
boundaryMode = cudaBoundaryModeTrap);
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template
void surf3Dread(Type* data,
surface 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.9.6
surf3Dwrite()
template
void surf3Dwrite(Type data,
surface 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.9.7
surf1DLayeredread()
template
Type surf1DLayeredread(
surface surfRef,
int x, int layer,
boundaryMode = cudaBoundaryModeTrap);
template
void surf1DLayeredread(Type data,
surface 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.9.8
surf1DLayeredwrite()
template
void surf1DLayeredwrite(Type data,
surface 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.
B.9.9
surf2DLayeredread()
template
Type surf2DLayeredread(
surface surfRef,
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int x, int y, int layer,
boundaryMode = cudaBoundaryModeTrap);
template
void surf2DLayeredread(Type data,
surface 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.9.10
surf2DLayeredwrite()
template
void surf2DLayeredwrite(Type data,
surface 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.9.11
surfCubemapread()
template
Type surfCubemapread(
surface surfRef,
int x, int y, int face,
boundaryMode = cudaBoundaryModeTrap);
template
void surfCubemapread(Type data,
surface 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.9.12
surfCubemapwrite()
template
void surfCubemapwrite(Type data,
surface 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.
B.9.13
surfCubemabLayeredread()
template
Type surfCubemapLayeredread(
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surface surfRef,
int x, int y, int layerFace,
boundaryMode = cudaBoundaryModeTrap);
template
void surfCubemapLayeredread(Type data,
surface 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.9.14
surfCubemapLayeredwrite()
template
void surfCubemapLayeredwrite(Type data,
surface 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
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 that the latter since threads are time sliced.
B.11
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 32-bit 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 can only be used in device functions and atomic
functions operating on mapped page-locked memory (Section 3.2.4.3) are not
atomic from the point of view of the host or other devices.
As mentioned in Table F-1, the support for atomic operations varies with the
compute capability:
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Atomic functions are only available for devices of compute capability 1.1 and
higher.
Atomic functions operating on 32-bit integer values in shared memory and
atomic functions operating on 64-bit integer values in global memory are only
available for devices of compute capability 1.2 and higher.
Atomic functions operating on 64-bit integer values in shared memory are only
available for devices of compute capability 2.x and higher.
Only atomicExch() and atomicAdd() can operate on 32-bit floating-point
values:
in global memory for atomicExch() and devices of compute capability
1.1 and higher.
in shared memory for atomicExch() and devices of compute capability
1.2 and higher.
in global and shared memory for atomicAdd() and devices of compute
capability 2.x and higher.
Note however that any atomic operation can be implemented based on
atomicCAS() (Compare And Swap). For example, atomicAdd() for
double-precision floating-point numbers can be implemented as follows:
__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)));
} while (assumed != old);
return __longlong_as_double(old);
}
B.11.1
Arithmetic Functions
B.11.1.1
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);
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 floating-point version of atomicAdd() is only supported by devices of
compute capability 2.x and higher.
B.11.1.2
atomicSub()
int atomicSub(int* address, int val);
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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.
B.11.1.3
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.11.1.4
atomicMin()
int atomicMin(int* address, int val);
unsigned int atomicMin(unsigned int* address,
unsigned int val);
reads the 32-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.
B.11.1.5
atomicMax()
int atomicMax(int* address, int val);
unsigned int atomicMax(unsigned int* address,
unsigned int val);
reads the 32-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.
B.11.1.6
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.
B.11.1.7
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.
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B.11.1.8
C Language Extensions
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.11.2
Bitwise Functions
B.11.2.1
atomicAnd()
int atomicAnd(int* address, int val);
unsigned int atomicAnd(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.
B.11.2.2
atomicOr()
int atomicOr(int* address, int val);
unsigned int atomicOr(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.
B.11.2.3
atomicXor()
int atomicXor(int* address, int val);
unsigned int atomicXor(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
C Language Extensions
Warp Vote Functions
Warp vote functions are only supported by devices of compute capability 1.2 and
higher (see Section 4.1 for the definition of a warp).
int __all(int predicate);
evaluates predicate for all active threads of the warp and returns non-zero if and
only if predicate evaluates to non-zero for all of them.
int __any(int predicate);
evaluates predicate for all active threads of the warp and returns non-zero if and
only if predicate evaluates to non-zero for any of them.
unsigned int __ballot(int predicate);
evaluates predicate for all active threads of the warp and returns an integer
whose Nth bit is set if and only if predicate evaluates to non-zero for the Nth
thread of the warp. This function is only supported by devices of compute
capability 2.x and higher.
B.13
Warp Shuffle Functions
__shfl, __shfl_up, __shfl_down, __shfl_xor exchange a variable between
threads within a warp.
They are only supported by devices of compute capability 3.0 (see Section 4.1 for
the definition of a warp).
B.13.1
Synopsys
int
int
int
int
__shfl(int var, int srcLane, int width=warpSize);
__shfl_up(int var, unsigned int delta, int width=warpSize);
__shfl_down(int var, unsigned int delta, int width=warpSize);
__shfl_xor(int var, int laneMask, int width=warpSize);
float __shfl(float var, int srcLane, int width=warpSize);
float __shfl_up(float var, unsigned int delta,
int width=warpSize);
float __shfl_down(float var, unsigned int delta,
int width=warpSize);
float __shfl_xor(float var, int laneMask, int width=warpSize);
B.13.2
Description
The __shfl() 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, moving 4 bytes of data per thread. Exchange of 8byte quantities must be broken into two separate invocations of __shfl().
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Threads within a warp are referred to as lanes, and for devices of compute capability
3.0 may have an index between 0 and 31 (inclusive). Four source-lane addressing
modes are supported:
__shfl(): Direct copy from indexed lane
__shfl_up(): Copy from a lane with lower ID relative to caller
__shfl_down(): Copy from a lane with higher ID relative to caller
__shfl_xor(): 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() command. If the target thread is inactive, the retrieved value is
undefined.
All the __shfl() intrinsics take an optional width parameter which permits subdivision of the warp into segments – for example to exchange data between 4
groups of 8 lanes in a SIMD manner. If width is less than 32 then each subsection
of the warp behaves as a separate entity with a starting logical lane ID of 0. A thread
may only exchange data with others in its own subsection. width must have a value
which is a power of 2 so that the warp can be subdivided equally; results are
undefined if width is not a power of 2, or is a number greater than warpSize.
__shfl() returns the value of var held by the thread whose ID is given by
srcLane. If srcLane is outside the range [0:width-1], then the thread’s own
value of var is returned.
__shfl_up() 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. The source lane index will not wrap around the
value of width, so effectively the lower delta lanes will be unchanged.
__shfl_down() 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. As for __shfl_up(), 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() 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 the resulting lane ID falls outside the range permitted by width, the
thread’s own value of var is returned. This mode implements a butterfly addressing
pattern such as is used in tree reduction and broadcast.
B.13.3
Return Value
All __shfl() 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.
B.13.4
Notes
All __shfl() intrinsics share the same semantics with respect to code motion as
the vote intrinsics __any() and __all().
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Threads may only read data from another thread which is actively participating in
the __shfl() 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.
Types other than int or float must first be cast in order to use the __shfl()
intrinsics.
B.13.5
Examples
B.13.5.1
Broadcast of a single value across a warp
__global__ void bcast(int arg) {
int value;
if (laneId == 0)
// Note unused variable for
value = arg;
// all threads except lane 0
value = __shfl(value, 0);
// Get “value” from lane 0
if (value != arg)
printf(“Thread %d failed.\n”, threadIdx.x);
}
void main() {
bcast<<< 1, 32 >>>(1234);
}
B.13.5.2
Inclusive plus-scan across sub-partitions of 8 threads
__global__ void scan4() {
// 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) {
// Note: shfl requires all threads being
// accessed to be active. Therefore we do
// the __shfl 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(value, i, 8);
if (laneId >= i)
value += n;
}
printf(“Thread %d final value = %d\n”, threadIdx.x, value);
}
void main() {
scan4<<< 1, 32 >>>();
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}
B.13.5.3
Reduction across a warp
__global__ void warpReduce() {
// 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(value, i, 32);
// “value” now contains the sum across all threads
printf(“Thread %d final value = %d\n”, threadIdx.x, value);
}
void main() {
warpReduce<<< 1, 32 >>>();
}
B.14
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 for the first multiprocessor can be obtained via the
CUDA profiler by listing prof_trigger_00, prof_trigger_01, …,
prof_trigger_07, etc. in the profiler.conf file (see the profiler manual for
more details). All counters are reset before each kernel call (note that when an
application is run via cuda-gdb, the Visual Profiler, or the Parallel Nsight CUDA
Debugger, all launches are synchronous).
B.15
Assertion
Assertion is only supported by devices of compute capability 2.x and higher.
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:
:::
block: [blockId.x,blockId.x,blockIdx.z],
thread: [threadIdx.x,threadIdx.y,threadIdx.z]
Assertion `` failed.
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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() is only supported
// for devices of compute capability 2.0 and higher
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ < 200)
#undef assert
#define assert(arg)
#endif
__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();
cudaDeviceReset();
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 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.16
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.
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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 Section B.16.4 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.16.1
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” will be accepted and printf will expect a double-precision variable in
the corresponding location in the argument list.
B.16.2
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-O/S-dependent.
As described in Section B.16.1, 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
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C Language Extensions
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
Section B.16.3). 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().
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.
B.16.3
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):
B.16.4
cudaDeviceGetLimit(size_t* size,cudaLimitPrintfFifoSize)
cudaDeviceSetLimit(cudaLimitPrintfFifoSize, size_t size)
Examples
The following code sample:
#include "stdio.h"
// printf() is only supported
// for devices of compute capability 2.0 and higher
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ < 200)
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Appendix B.
C Language Extensions
#define printf(f, ...) ((void)(f, __VA_ARGS__),0)
#endif
__global__ void helloCUDA(float f)
{
printf("Hello thread %d, f=%f\n", threadIdx.x, f);
}
int main()
{
helloCUDA<<<1, 5>>>(1.2345f);
cudaDeviceReset();
return 0;
}
will output:
Hello
Hello
Hello
Hello
Hello
thread
thread
thread
thread
thread
2,
1,
4,
0,
3,
f=1.2345
f=1.2345
f=1.2345
f=1.2345
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.
The following code sample:
#include "stdio.h"
// printf() is only supported
// for devices of compute capability 2.0 and higher
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ < 200)
#define printf(f, ...) ((void)(f, __VA_ARGS__),0)
#endif
__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);
cudaDeviceReset();
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.
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Appendix B.
B.17
C Language Extensions
Dynamic Global Memory Allocation
Dynamic global memory allocation is 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.
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 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.17.1
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 Section G.2), or implicitly
via the CUDA runtime API (see Section 3.2). 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().
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Appendix B.
B.17.2
C Language Extensions
Interoperability with Host Memory API
Memory allocated via malloc() cannot be freed using the runtime (i.e. by calling
any of the free memory functions from Sections 3.2.2).
Similarly, memory allocated via the runtime (i.e. by calling any of the memory
allocation functions from Sections 3.2.2) cannot be freed via free().
Memory allocated via malloc() can be copied using the runtime (i.e. by calling any
of the copy memory functions from Sections 3.2.2).
B.17.3
Examples
B.17.3.1
Per Thread Allocation
The following code sample:
#include
#include
__global__ void mallocTest()
{
char* ptr = (char*)malloc(123);
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
Thread
Thread
Thread
Thread
0
1
2
3
4
got
got
got
got
got
pointer:
pointer:
pointer:
pointer:
pointer:
00057020
0005708c
000570f8
00057164
000571d0
Notice how each thread encounters the malloc() command and so receives its
own allocation. (Exact pointer values will vary: these are illustrative.)
B.17.3.2
Per Thread Block Allocation
#include
__global__ void mallocTest()
{
__shared__ int* data;
// The first thread in the block does the allocation
// and then shares the pointer with all other threads
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Appendix B.
C Language Extensions
// through shared memory, so that access can easily be
// coalesced. 64 bytes per thread are allocated.
if (threadIdx.x == 0)
data = (int*)malloc(blockDim.x * 64);
__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;
}
B.17.3.3
Allocation Persisting Between Kernel Launches
#include
#include
#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
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Appendix B.
C Language Extensions
__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;
}
B.18
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 Section 3.2.5.5 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 Section B.3.2) and specifies the dimension and size of
the grid, such that Dg.x * Dg.y * Dg.z equals the number of blocks being
launched; Dg.z must be equal to 1 for devices of compute capability 1.x;
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Appendix B.
C Language Extensions
Db is of type dim3 (see Section B.3.2) 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 Section B.2.3; 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 and like the function arguments, are currently passed via shared
memory to the device.
The function call will fail if Dg or Db are greater than the maximum sizes allowed
for the device as specified in Appendix F, 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, functions arguments (for devices of compute
capability 1.x), and execution configuration.
B.19
Launch Bounds
As discussed in detail in Section 5.2.3, 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 Section 5.3.2.2) and instruction count to a minimum. 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.
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Appendix B.
C Language Extensions
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 Section 4.2 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
Section 5.2.3) 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.
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 Section 3.1.4.
#define THREADS_PER_BLOCK
#if __CUDA_ARCH__ >= 200
#define MY_KERNEL_MAX_THREADS
#define MY_KERNEL_MIN_BLOCKS
#else
#define MY_KERNEL_MAX_THREADS
#define MY_KERNEL_MIN_BLOCKS
#endif
256
(2 * THREADS_PER_BLOCK)
3
THREADS_PER_BLOCK
2
// 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<<>>(...);
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Appendix B.
C Language Extensions
This will not work however since __CUDA_ARCH__ is undefined in host code as
mentioned in Section 3.1.4, 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<<>>(...);
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<<>>(...);
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 Section 5.2.3 for 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.
B.20
#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 a number that specifies how many times the loop must be
unrolled.
For example, in this code sample:
#pragma unroll 5
for (int i = 0; i < n; ++i)
the loop will be unrolled 5 times. The compiler will also insert code to ensure
correctness (in the example above, to ensure that there will only be n iterations if n
is less than 5, for example). It is up to the programmer to make sure that the
specified unroll number gives the best performance.
#pragma unroll 1 will prevent the compiler from ever unrolling a loop.
If no number is specified after #pragma unroll, the loop is completely unrolled
if its trip count is constant, otherwise it is not unrolled at all.
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Appendix C.
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.
C.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.
C.1.1
Single-Precision Floating-Point Functions
Addition and multiplication are IEEE-compliant, so have a maximum error of
0.5 ulp. However, on the device, the compiler often combines them into a single
multiply-add instruction (FMAD) and for devices of compute capability 1.x, FMAD
truncates the intermediate result of the multiplication as mentioned in Section F.2.
This combination can be avoided by using the __fadd_[rn,rz,ru,rd]() and
__fmul_[rn,rz,ru,rd]() intrinsic functions (see Section C.2).
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.
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Appendix C.
Mathematical Functions
Table C-1.
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.
116
Function
x+y
Maximum ulp error
x*y
0 (IEEE-754 round-to-nearest-even)
(except for devices of compute capability 1.x when
multiplication is merged into an FMAD)
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)
2 (full range)
hypotf(x,y)
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)
log2f(x)
3 (full range)
log10f(x)
3 (full range)
log1pf(x)
2 (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)
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)
0 (IEEE-754 round-to-nearest-even)
(except for devices of compute capability 1.x when addition is
merged into an FMAD)
CUDA C Programming Guide Version 4.2
Appendix C.
Mathematical Functions
Function
coshf(x)
Maximum ulp error
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)
3 (full range)
erfcf(x)
6 (full range)
erfinvf(x)
3 (full range)
erfcinvf(x)
7 (full range)
erfcxf(x)
6 (full range)
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
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)
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)
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2 (full range)
117
Appendix C.
C.1.2
Mathematical Functions
Function
lroundf(x)
Maximum ulp error
llrintf(x)
0 (full range)
llroundf(x)
0 (full range)
0 (full range)
Double-Precision Floating-Point Functions
The errors listed below only apply when compiling for devices with native doubleprecision support. When compiling for devices without such support, such as
devices of compute capability 1.2 and lower, the double type gets demoted to
float by default and the double-precision math functions are mapped to their
single-precision equivalents.
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 C-2
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.
118
Function
x+y
Maximum ulp error
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)
hypot(x,y)
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)
2 (full range)
cos(x)
2 (full range)
0 (IEEE-754 round-to-nearest-even)
CUDA C Programming Guide Version 4.2
Appendix C.
Mathematical Functions
Function
tan(x)
Maximum ulp error
sincos(x,sptr,cptr)
2 (full range)
sinpi(x)
2 (full range)
cospi(x)
2 (full range)
asin(x)
2 (full range)
acos(x)
2 (full range)
atan(x)
2 (full range)
atan2(y,x)
2 (full range)
sinh(x)
1 (full range)
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)
8 (full range)
erfcx(x)
3 (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
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
fmod(x,y)
0 (full range)
remainder(x,y)
0 (full range)
remquo(x,y,iptr)
0 (full range)
modf(x,iptr)
0 (full range)
CUDA C Programming Guide Version 4.2
2 (full range)
119
Appendix C.
C.2
Mathematical Functions
Function
fdim(x,y)
Maximum ulp error
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)
0 (full range)
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 Section C.1. 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 C-3
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 C-3. 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.
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Appendix C.
Mathematical Functions
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.
C.2.1
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, additions and multiplications generated from the '*' and '+' operators will
frequently be combined into FMADs.
The accuracy of floating-point division varies depending on the compute capability
of the device and whether the code is compiled with -prec-div=false or
-prec-div=true. For devices of compute capability 1.x or for devices of
compute capability 2.x and higher 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 C-4. 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 on devices of compute capability 2.x and higher when
the code is compiled with -prec-div=true or without any -prec-div option at
all since its default value is true.
Table C-4.
Single-Precision Floating-Point Intrinsic Functions
Supported by the CUDA Runtime Library with
Respective Error Bounds
Function
__fadd_[rn,rz,ru,rd](x,y)
Error bounds
__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.
__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
IEEE-compliant.
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
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Appendix C.
Mathematical Functions
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.
__sinf(x)
For x in [-, ], the maximum absolute error
is 2-21.41, and larger otherwise.
__cosf(x)
For x in [-, ], 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)
C.2.2
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 C-5.
122
Double-Precision Floating-Point Intrinsic
Functions Supported by the CUDA Runtime
Library with Respective Error Bounds
Function
__dadd_[rn,rz,ru,rd](x,y)
Error bounds
__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
IEEE-compliant.
CUDA C Programming Guide Version 4.2
Appendix D.
C/C++ Language Support
As described in Section 3.1, source files compiled with nvcc can include a mix of
host code and device code.
For the host code, nvcc supports whatever part of the C++ ISO/IEC 14882:2003
specification the host c++ compiler supports.
For the device code, nvcc supports the features illustrated in Section D.1 with
some restrictions described in Section D.2; it does not support run time type
information (RTTI), exception handling, and the C++ Standard Library.
D.1
Code Samples
D.1.1
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)
{
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Appendix D.
C++ Language Support
PixelRGBA p1, p2;
// ...
// Initialization of p1 and p2 here
PixelRGBA p3 = p1 + p2;
}
D.1.2
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;
}
D.1.3
Class Template
template
class myValues {
T values[MAX_VALUES];
public:
__device__ myValues(T clear) { ... }
__device__ void setValue(int Idx, T value) { ... }
__device__ void putToMemory(T* valueLocation) { ... }
};
template
void __global__ useValues(T* memoryBuffer) {
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Appendix D.
C++ Language Support
myValues myLocation(0);
...
}
__device__ void* buffer;
int main()
{
...
useValues<<>>(buffer);
...
}
D.1.4
Function Template
template
__device__ bool func(T x)
{
...
return (...);
}
template <>
__device__ bool func(T x) // Specialization
{
return true;
}
// Explicit argument specification
bool result = func(0.5);
// Implicit argument deduction
int x = 1;
bool result = func(x);
D.1.5
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;
}
};
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125
Appendix D.
C++ Language Support
// Device code
template __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<<>>(v1, v2, v3, N, Add());
...
}
D.2
Restrictions
D.2.1
Qualifiers
D.2.1.1
Device Memory Qualifiers
The __device__, __shared__ and __constant__ qualifiers are not allowed
on:
class, struct, and union data members,
formal parameters,
local variables within a function that executes on the host.
__shared__ and __constant__ variables have implied static storage.
__device__ and __constant__ variables are only allowed at file scope.
__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 Section B.2.3.
D.2.1.2
Volatile Qualifier
Only after the execution of a __threadfence_block(), __threadfence(),
or __syncthreads() (Sections B.5 and B.6) are prior writes to global or shared
memory guaranteed to be visible by other threads. As long as this requirement is
met, the compiler is free to optimize reads and writes to global or shared memory.
This behavior can be changed 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.
For example, in the code sample of Section 5.4.3, if s_ptr were not declared as
volatile, the compiler would optimize away the store to shared memory for each
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Appendix D.
C++ Language Support
assignment to s_ptr[tid]. It would accumulate the result into a register instead
and only store the final result to shared memory, which would be incorrect.
D.2.2
Pointers
For devices of compute capability 1.x, pointers in code that is executed on the
device are supported as long as the compiler is able to resolve whether they point to
either the shared memory space, the global memory space, or the local memory
space, otherwise they are restricted to only point to memory allocated or declared in
the global memory space. For devices of compute capability 2.x and higher, pointers
are supported without any restriction.
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 Section 3.2.2 can only be used in
host code.
D.2.3
Operators
D.2.3.1
Assignment Operator
__constant__ variables can only be assigned from the host code through runtime
functions (Sections 3.2.2); 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
Section B.4.
D.2.3.2
Address Operator
It is not allowed to take the address of any of the built-in variables defined in
Section B.4.
D.2.4
Functions
D.2.4.1
Function Parameters
__global__ function parameters are passed to the device:
via shared memory and are limited to 256 bytes on devices of compute
capability 1.x,
via constant memory and are limited to 4 KB on devices of compute
capability 2.x and higher.
__device__ and __global__ functions cannot have a variable number of
arguments.
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127
Appendix D.
D.2.4.2
C++ Language Support
Static Variables within Function
Static variables cannot be declared within the body of __device__ and
__global__ functions.
D.2.4.3
Function Pointers
Function pointers to __global__ functions are supported in host code, but not in
device code.
Function pointers to __device__ functions are only supported in device code
compiled for devices of compute capability 2.x and higher.
It is not allowed to take the address of a __device__ function in host code.
D.2.4.4
Function Recursion
__global__ functions do not support recursion.
__device__ functions only support recursion in device code compiled for devices
of compute capability 2.x and higher.
D.2.5
Classes
D.2.5.1
Data Members
Static data members are not supported.
The layout of bit-fields in device code may currently not match the layout in host
code on Windows.
D.2.5.2
Function Members
Static member functions cannot be __global__ functions.
D.2.5.3
Constructors and Destructors
Declaring global variables for which a constructor or a destructor needs to be called
is not supported.
D.2.5.4
Virtual Functions
Declaring global variables of a class with virtual functions is not supported.
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.
D.2.5.5
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.
D.2.5.6
Windows-Specific
On Windows, the CUDA compiler may produce a different memory layout,
compared to the host Microsoft compiler, for a C++ object of class type T that
satisfies any of the following conditions:
T has virtual functions or derives from a direct or indirect base class that has
virtual functions;
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Appendix D.
C++ Language Support
T has a direct or indirect virtual base class;
T has multiple inheritance with more than one direct or indirect empty base
class.
The size for such an object may also be different in host and device code. As long as
type T is used exclusively in host or device code, the program should work correctly.
Do not pass objects of type T between host and device code (e.g. as arguments to
__global__ functions or through cudaMemcpy*() calls).
D.2.6
Templates
A __global__ function template cannot be instantiated with a type or typedef that
is defined within a function or is private to a class or structure, as illustrated in the
following code sample:
template
__global__ void myKernel1(void) { }
template
__global__ void myKernel2(T par) { }
class myClass {
private:
struct inner_t { };
public:
static void launch(void)
{
// Both kernel launches below are disallowed
// as myKernel1 and myKernel2 are instantiated
// with private type inner_t
myKernel1<<<1,1>>>();
inner_t var;
myKernel2<<<1,1>>>(var);
}
};
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129
Appendix E.
Texture Fetching
This appendix gives the formula used to compute the value returned by the texture
functions of Section B.8 depending on the various attributes of the texture reference
(see Section 3.2.10).
The texture bound to the texture reference is represented as an array T of
N texels for a one-dimensional texture,
N M texels for a two-dimensional texture,
N M 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
Section 3.2.10.1.2. In this appendix, the coordinates are assumed to be in the valid
range. Section 3.2.10.1.2 explained how out-of-range coordinates are remapped to
the valid range based on the addressing mode.
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131
Appendix E. Texture Fetching
E.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 E-1 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 Section 3.2.10.1.1).
tex(x)
T[3]
T[0]
T[2]
T[1]
x
0
1
2
3
4
Non-Normalized
0
0.25
0.5
0.75
1
Normalized
Figure E-1. Nearest-Point Sampling of a One-Dimensional
Texture of Four Texels
E.2
Linear Filtering
In this filtering mode, which is only available for floating-point textures, the value
returned by the texture fetch is
132
tex( x) (1 )T [i] T [i 1] for a one-dimensional texture,
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Appendix E.
Texture Fetching
tex( x, y) (1 )(1 )T [i, j ] (1 )T [i 1, j ] (1 )T [i, j 1] T [i 1, j 1]
for a two-dimensional texture,
tex( x, y, z )
(1 )(1 )(1 )T [i, j , k ] (1 )(1 )T [i 1, j , k ]
(1 ) (1 )T [i, j 1, k ] (1 )T [i 1, j 1, k ]
(1 )(1 )T [i, j , k 1] (1 )T [i 1, j , k 1]
(1 ) T [i, j 1, k 1] T [i 1, j 1, k 1]
for a three-dimensional texture,
where:
i floor( x B ) , frac( x B ) , x B x 0.5 ,
j floor( y B ) , frac( y B ) , y B y 0.5 ,
k floor( z B ) , frac( z B ) , z B z 0.5 .
, , and are stored in 9-bit fixed point format with 8 bits of fractional value
(so 1.0 is exactly represented).
Figure E-2 illustrates nearest-point sampling for a one-dimensional texture with
N 4.
tex(x)
T[3]
T[0]
T[2]
T[1]
x
0
1
2
3
4
Non-Normalized
0
0.25
0.5
0.75
1
Normalized
Figure E-2. Linear Filtering of a One-Dimensional Texture of
Four Texels in Clamp Addressing Mode
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133
Appendix E. Texture Fetching
E.3
Table Lookup
A table lookup TL(x) where
TL( x) tex (
x
spans the interval [0, R] can be implemented as
N 1
x 0.5) in order to ensure that TL(0) T [0] and TL( R) T [ N 1] .
R
Figure E-3 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 .
TL(x)
T[3]
T[0]
T[2]
T[1]
x
0
4/3
8/3
4
0
1/3
2/3
1
Figure E-3. One-Dimensional Table Lookup Using Linear
Filtering
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Appendix F.
Compute Capabilities
The general specifications and features of a compute device depend on its compute
capability (see Section 2.5).
Section F.1 gives the features and technical specifications associated to each
compute capability.
Section F.2 reviews the compliance with the IEEE floating-point standard.
Section F.3, F.4, and F.5 give more details on the architecture of devices of compute
capability 1.x, 2.x, and 3.0, respectively.
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135
Appendix F.
F.1
Compute Capabilities
Features and Technical Specifications
Table F-1.
Feature Support per Compute Capability
Compute Capability
Feature Support
(Unlisted features are supported
for all compute capabilities)
Atomic functions operating on 32-bit
integer values in global memory
(Section B.11)
atomicExch() operating on 32-bit
floating point values in global
memory (Section B.11.1.3)
1.0
1.1
1.2
No
2.x
3.0
1.3
Yes
Atomic functions operating on 32-bit
integer values in shared memory
(Section B.11)
atomicExch() operating on 32-bit
floating point values in shared
memory (Section B.11.1.3)
No
Yes
Atomic functions operating on 64-bit
integer values in global memory
(Section B.11)
Warp vote functions (Section B.12)
Double-precision floating-point
numbers
No
Yes
Atomic functions operating on 64-bit
integer values in shared memory
(Section B.11)
Atomic addition operating on 32-bit
floating point values in global and
shared memory (Section B.11.1.1)
__ballot() (Section B.12)
No
Yes
__threadfence_system() (Section B.5)
__syncthreads_count(),
__syncthreads_and(),
__syncthreads_or() (Section B.6)
Surface functions (Section B.9)
3D grid of thread blocks
Table F-2.
Technical Specifications per Compute Capability
Compute Capability
Technical
Specifications
Maximum dimensionality
of grid of thread blocks
Maximum x-dimension of
a grid of thread blocks
Maximum y- or z-
136
1.0
1.1
1.2
1.3
2
2.x
3.0
3
65535
231-1
65535
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Appendix F.
Compute Capabilities
Compute Capability
Technical
Specifications
1.0
1.1
1.2
1.3
2.x
3.0
dimension of a grid of
thread blocks
Maximum dimensionality
of thread block
3
Maximum x- or ydimension of a block
512
1024
Maximum z-dimension of
a block
64
Maximum number of
threads per block
512
1024
Warp size
32
Maximum number of
resident blocks per
multiprocessor
8
16
Maximum number of
resident warps per
multiprocessor
24
32
48
64
Maximum number of
resident threads per
multiprocessor
768
1024
1536
2048
Number of 32-bit
registers per
multiprocessor
8K
16 K
32 K
64 K
Maximum amount of
shared memory per
multiprocessor
Number of shared
memory banks
Amount of local memory
per thread
16 KB
48 KB
16
32
16 KB
512 KB
Constant memory size
64 KB
Cache working set per
multiprocessor for
constant memory
8 KB
Cache working set per
multiprocessor for
texture memory
Device dependent, between 6 KB and 8 KB
Maximum width for a 1D
texture reference bound
to a CUDA array
8192
Maximum width for a 1D
texture reference bound
to linear memory
65536
227
Maximum width and
number of layers for a
1D layered texture
reference
8192 x 512
16384 x 2048
Maximum width and
height for a 2D texture
reference bound to a
CUDA array
65536 x 32768
65536 x 65535
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Appendix F.
Compute Capabilities
Compute Capability
Technical
Specifications
Maximum width and
height for a 2D texture
reference bound to linear
memory
Maximum width and
height for a 2D texture
reference bound to a
CUDA array supporting
texture gather
Maximum width, height,
and number of layers for
a 2D layered texture
reference
Maximum width, height,
and depth for a 3D
texture reference bound
to a CUDA array
1.0
1.1
1.2
2.x
3.0
65000 x 65000
65000 x 65000
N/A
16384 x 16384
8192 x 8192 x 512
16384 x 16384 x
2048
2048 x 2048 x 2048
4096 x
4096 x
4096
Maximum width (and
height) for a cubemap
texture reference
N/A
16384
Maximum width (and
height) and number of
layers for a cubemap
layered texture reference
N/A
16384 x 2046
Maximum number of
textures that can be
bound to a kernel
128
Maximum width for a 1D
surface reference bound
to a CUDA array
256
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
138
1.3
N/A
65536 x 32768 x
2048
Maximum width, height,
and depth for a 3D
surface reference bound
to a CUDA array
65536 x 32768 x
2048
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
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Appendix F.
Compute Capabilities
Compute Capability
Technical
Specifications
1.0
1.1
1.2
Maximum number of
surfaces that can be
bound to a kernel
Maximum number of
instructions per kernel
F.2
2 million
1.3
2.x
3.0
8
16
512 million
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;
For single-precision floating-point numbers on devices of compute
capability 1.x:
Denormalized numbers are not supported; floating-point arithmetic and
comparison instructions convert denormalized operands to zero prior to
the floating-point operation;
Underflowed results are flushed to zero;
Some instructions are not IEEE-compliant:
Addition and multiplication are often combined into a single multiplyadd instruction (FMAD), which truncates (i.e. without rounding) the
intermediate mantissa of the multiplication;
Division is implemented via the reciprocal in a non-standard-compliant
way;
Square root is implemented via the reciprocal square root in a nonstandard-compliant way;
For addition and multiplication, only round-to-nearest-even and
round-towards-zero are supported via static rounding modes; directed
rounding towards +/- infinity is not supported;
To mitigate the impact of these restrictions, IEEE-compliant software (and
therefore slower) implementations are provided through the following
intrinsics (c.f. Section C.2.1):
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__fmaf_r{n,z,u,d}(float, float, float): single-precision
fused multiply-add with IEEE rounding modes,
__frcp_r[n,z,u,d](float): single-precision reciprocal with
IEEE rounding modes,
__fdiv_r[n,z,u,d](float, float): single-precision division
with IEEE rounding modes,
__fsqrt_r[n,z,u,d](float): single-precision square root with
IEEE rounding modes,
__fadd_r[u,d](float, float): single-precision addition with
IEEE directed rounding,
__fmul_r[u,d](float, float): single-precision multiplication
with IEEE directed rounding;
For double-precision floating-point numbers on devices of compute
capability 1.x:
Round-to-nearest-even is the only supported IEEE rounding mode for
reciprocal, division, and square root.
When compiling for devices without native double-precision floating-point support,
i.e. devices of compute capability 1.2 and lower, each double variable is converted
to single-precision floating-point format (but retains its size of 64 bits) and doubleprecision floating-point arithmetic gets demoted to single-precision floating-point
arithmetic.
For devices of compute capability 2.x and higher, 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); code compiled with -ftz=true, -prec-div=false,
and -prec-sqrt=false comes closest to the code generated for devices of
compute capability 1.x.
Addition and multiplication are often combined into a single multiply-add
instruction:
FMAD for single precision on devices of compute capability 1.x,
FFMA for single precision on devices of compute capability 2.x and higher.
As mentioned above, FMAD truncates the mantissa prior to use it in the addition.
FFMA, on the other hand, is an IEEE-754(2008) compliant fused multiply-add
instruction, so the full-width product is being used in the addition and a single
rounding occurs during generation of the final result. While FFMA in general has
superior numerical properties compared to FMAD, the switch from FMAD to
FFMA can cause slight changes in numeric results and can in rare circumstances
lead to slighty larger error in final results.
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.
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.
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http://developer.nvidia.com/content/precision-performance-floating-point-andieee-754-compliance-nvidia-gpus includes more information on the floating point
accuracy and compliance of NVIDIA GPUs.
F.3
Compute Capability 1.x
F.3.1
Architecture
For devices of compute capability 1.x, a multiprocessor consists of:
8 CUDA cores for arithmetic operations (see Section 5.4.1 for throughputs of
arithmetic operations),
1 double-precision floating-point unit for double-precision floating-point
arithmetic operations,
2 special function units for single-precision floating-point transcendental
functions (these units can also handle single-precision floating-point
multiplications),
1 warp scheduler.
To execute an instruction for all threads of a warp, the warp scheduler must
therefore issue the instruction over:
4 clock cycles for an integer or single-precision floating-point arithmetic
instruction,
32 clock cycles for a double-precision floating-point arithmetic instruction,
16 clock cycles for a single-precision floating-point transcendental instruction.
A multiprocessor also 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.
Multiprocessors are grouped into Texture Processor Clusters (TPCs). The number of
multiprocessors per TPC is:
2 for devices of compute capabilities 1.0 and 1.1,
3 for devices of compute capabilities 1.2 and 1.3.
Each TPC has a read-only texture cache that is shared by all multiprocessors and
speeds up reads from the texture memory space, which resides in device memory.
Each multiprocessor accesses the texture cache via a texture unit that implements
the various addressing modes and data filtering mentioned in Section 3.2.10.
The local and global memory spaces reside in device memory and are not cached.
F.3.2
Global Memory
A global memory request for a warp is split into two memory requests, one for each
half-warp, that are issued independently. Sections F.3.2.1 and F.3.2.2 describe how
the memory accesses of threads within a half-warp are coalesced into one or more
memory transactions depending on the compute capability of the device. Figure F-1
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shows some examples of global memory accesses and corresponding memory
transactions based on compute capability.
The resulting memory transactions are serviced at the throughput of device
memory.
F.3.2.1
Devices of Compute Capability 1.0 and 1.1
To coalesce, the memory request for a half-warp must satisfy the following
conditions:
The size of the words accessed by the threads must be 4, 8, or 16 bytes;
If this size is:
4, all 16 words must lie in the same 64-byte segment,
8, all 16 words must lie in the same 128-byte segment,
16, the first 8 words must lie in the same 128-byte segment and the last 8
words in the following 128-byte segment;
Threads must access the words in sequence: The kth thread in the half-warp
must access the kth word.
If the half-warp meets these requirements, a 64-byte memory transaction, a 128-byte
memory transaction, or two 128-byte memory transactions are issued if the size of
the words accessed by the threads is 4, 8, or 16, respectively. Coalescing is achieved
even if the warp is divergent, i.e. there are some inactive threads that do not actually
access memory.
If the half-warp does not meet these requirements, 16 separate 32-byte memory
transactions are issued.
F.3.2.2
Devices of Compute Capability 1.2 and 1.3
Threads can access any words in any order, including the same words, and a single
memory transaction for each segment addressed by the half-warp is issued. This is
in contrast with devices of compute capabilities 1.0 and 1.1 where threads need to
access words in sequence and coalescing only happens if the half-warp addresses a
single segment.
More precisely, the following protocol is used to determine the memory transactions
necessary to service all threads in a half-warp:
Find the memory segment that contains the address requested by the active
thread with the lowest thread ID. The segment size depends on the size of the
words accessed by the threads:
32 bytes for 1-byte words,
64 bytes for 2-byte words,
128 bytes for 4-, 8- and 16-byte words.
Find all other active threads whose requested address lies in the same segment.
Reduce the transaction size, if possible:
If the transaction size is 128 bytes and only the lower or upper half is used,
reduce the transaction size to 64 bytes;
If the transaction size is 64 bytes (originally or after reduction from 128
bytes) and only the lower or upper half is used, reduce the transaction size
to 32 bytes.
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Carry out the transaction and mark the serviced threads as inactive.
Repeat until all threads in the half-warp are serviced.
F.3.3
Shared Memory
Shared memory has 16 banks that are organized such that successive 32-bit words
map to successive banks. Each bank has a bandwidth of 32 bits per two clock
cycles.
A shared memory request for a warp is split into two memory requests, one for each
half-warp, that are issued independently. As a consequence, there can be no bank
conflict between a thread belonging to the first half of a warp and a thread
belonging to the second half of the same warp.
If a non-atomic instruction executed by a warp writes to the same location in shared
memory for more than one of the threads of the warp, only one thread per halfwarp performs a write and which thread performs the final write is undefined.
F.3.3.1
32-Bit Strided Access
A common access pattern is for each thread to access a 32-bit word from an array
indexed by the thread ID tid and with some stride s:
extern __shared__ float shared[];
float data = shared[BaseIndex + s * tid];
In this case, threads tid and tid+n access the same bank whenever s*n is a
multiple of the number of banks (i.e. 16) or, equivalently, whenever n is a multiple
of 16/d where d is the greatest common divisor of 16 and s. As a consequence,
there will be no bank conflict only if half the warp size (i.e. 16) is less than or equal
to 16/d., that is only if d is equal to 1, i.e. s is odd.
Figure F-2 shows some examples of strided access for devices of compute
capability 3.0. The same examples apply for devices of compute capability 1.x, but
with 16 banks instead of 32. Also, the access pattern for the example in the middle
generates 2-way bank conflicts for devices of compute capability 1.x.
F.3.3.2
32-Bit Broadcast Access
Shared memory features a broadcast mechanism whereby a 32-bit word can be read
and broadcast to several threads simultaneously when servicing one memory read
request. This reduces the number of bank conflicts when several threads read from
an address within the same 32-bit word. More precisely, a memory read request
made of several addresses is serviced in several steps over time by servicing one
conflict-free subset of these addresses per step until all addresses have been
serviced; at each step, the subset is built from the remaining addresses that have yet
to be serviced using the following procedure:
Select one of the words pointed to by the remaining addresses as the broadcast
word;
Include in the subset:
All addresses that are within the broadcast word,
One address for each bank (other than the broadcasting bank) pointed to
by the remaining addresses.
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Which word is selected as the broadcast word and which address is picked up for
each bank at each cycle are unspecified.
A common conflict-free case is when all threads of a half-warp read from an address
within the same 32-bit word.
Figure F-3 shows some examples of memory read accesses that involve the
broadcast mechanism for devices of compute capability 3.0. The same examples
apply for devices of compute capability 1.x, but with 16 banks instead of 32. Also,
the access pattern for the example at the right generates 2-way bank conflicts for
devices of compute capability 1.x.
F.3.3.3
8-Bit and 16-Bit Access
8-bit and 16-bit accesses typically generate bank conflicts. For example, there are
bank conflicts if an array of char is accessed the following way:
extern __shared__ float shared[];
char data = shared[BaseIndex + tid];
because shared[0], shared[1], shared[2], and shared[3], for example,
belong to the same bank. There are no bank conflicts however, if the same array is
accessed the following way:
char data = shared[BaseIndex + 4 * tid];
F.3.3.4
Larger Than 32-Bit Access
Accesses that are larger than 32-bit per thread are split into 32-bit accesses that
typically generate bank conflicts.
For example, there are 2-way bank conflicts for arrays of doubles accessed as
follows:
extern __shared__ float shared[];
double data = shared[BaseIndex + tid];
as the memory request is compiled into two separate 32-bit requests with a stride of
two. One way to avoid bank conflicts in this case is two split the double operands
like in the following sample code:
__shared__ int shared_lo[32];
__shared__ int shared_hi[32];
double dataIn;
shared_lo[BaseIndex + tid] = __double2loint(dataIn);
shared_hi[BaseIndex + tid] = __double2hiint(dataIn);
double dataOut =
__hiloint2double(shared_hi[BaseIndex + tid],
shared_lo[BaseIndex + tid]);
This might not always improve performance however and does perform worse on
devices of compute capabilities 2.x and higher.
The same applies to structure assignments. The following code, for example:
extern __shared__ float shared[];
struct type data = shared[BaseIndex + tid];
results in:
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struct type {
float x, y, z;
};
since each member is accessed with an odd stride of three 32-bit words;
Two separate reads with bank conflicts if type is defined as
struct type {
float x, y;
};
since each member is accessed with an even stride of two 32-bit words.
F.4
Compute Capability 2.x
F.4.1
Architecture
For devices of compute capability 2.x, a multiprocessor consists of:
For devices of compute capability 2.0:
32 CUDA cores for arithmetic operations (see Section 5.4.1 for
throughputs of arithmetic operations),
4 special function units for single-precision floating-point transcendental
functions,
For devices of compute capability 2.1:
48 CUDA cores for arithmetic operations (see Section 5.4.1 for
throughputs of arithmetic operations),
8 special function units for single-precision floating-point transcendental
functions,
2 warp schedulers.
At every instruction issue time, each scheduler issues:
One instruction for devices of compute capability 2.0,
Two independent instructions for devices of compute capability 2.1,
for some warp that is ready to execute, if any. The first scheduler is in charge of the
warps with an odd ID and the second scheduler is in charge of the warps with an
even ID. Note that when a scheduler issues a double-precision floating-point
instruction, the other scheduler cannot issue any instruction.
A warp scheduler can issue an instruction to only half of the CUDA cores. To
execute an instruction for all threads of a warp, a warp scheduler must therefore
issue the instruction over two clock cycles for an integer or floating-point arithmetic
instruction.
A multiprocessor also 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, both of which are used to cache accesses to local or global
memory, including temporary register spills. The cache behavior (e.g. whether reads
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are cached in both L1 and L2 or in L2 only) can be partially configured on a peraccess basis using modifiers to the load or store instruction.
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 and 48 KB of L1 cache, using
cudaFuncSetCacheConfig()/cuFuncSetCacheConfig():
// Device code
__global__ void MyKernel()
{
...
}
// Host code
// Runtime API
// cudaFuncCachePreferShared: shared memory is 48 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.
Applications may query the L2 cache size by checking the l2CacheSize device
property (see Section 3.2.6.1). The maximum L2 cache size is 768 KB.
Multiprocessors are grouped into Graphics Processor Clusters (GPCs). A GPC includes
four multiprocessors.
Each multiprocessor has a read-only texture cache to speed up reads from the
texture memory space, which resides in device memory. It accesses the texture cache
via a texture unit that implements the various addressing modes and data filtering
mentioned in Section 3.2.10.
F.4.2
Global Memory
Global memory accesses are cached. Using the –dlcm compilation flag, they can be
configured at compile time to be cached in both L1 and L2 (-Xptxas -dlcm=ca)
(this is the default setting) or in L2 only (-Xptxas -dlcm=cg).
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.
Figure F-1 shows some examples of global memory accesses and corresponding
memory transactions based on compute capability.
F.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 two clock
cycles.
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 unlike for devices of compute capability
1.x, multiple words can be broadcast in a single transaction) and for write accesses,
each address is written by only one of the threads (which thread performs the write
is undefined).
This means, in particular, that unlike for devices of compute capability 1.x, there are
no bank conflicts if an array of char is accessed as follows, for example:
extern __shared__ float shared[];
char data = shared[BaseIndex + tid];
Also, unlike for devices of compute capability 1.x, there may be bank conflicts
between a thread belonging to the first half of a warp and a thread belonging to the
second half of the same warp.
Figure F-3 shows some examples of memory read accesses that involve the
broadcast mechanism for devices of compute capability 3.0. The same examples
apply for devices of compute capability 2.x.
F.4.3.1
32-Bit Strided Access
A common access pattern is for each thread to access a 32-bit word from an array
indexed by the thread ID tid and with some stride s:
extern __shared__ float shared[];
float data = shared[BaseIndex + s * tid];
In this case, threads tid and tid+n access the same bank whenever s*n is a
multiple of the number of banks (i.e. 32) or, equivalently, whenever n is a multiple
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of 32/d where d is the greatest common divisor of 32 and s. As a consequence,
there will be no bank conflict only if the warp size (i.e. 32) is less than or equal to
32/d., that is only if d is equal to 1, i.e. s is odd.
Figure F-2 shows some examples of strided access for devices of compute
capability 3.0. The same examples apply for devices of compute capability 2.x.
However, the access pattern for the example in the middle generates 2-way bank
conflicts for devices of compute capability 2.x.
F.4.3.2
Larger Than 32-Bit Access
64-bit and 128-bit accesses are specifically handled to minimize bank conflicts as
described below.
Other accesses larger than 32-bit are split into 32-bit, 64-bit, or 128-bit accesses.
The following code, for example:
struct type {
float x, y, z;
};
extern __shared__ float shared[];
struct type data = shared[BaseIndex + tid];
results in three separate 32-bit reads without bank conflicts since each member is
accessed with a stride of three 32-bit words.
64-Bit Accesses
For 64-bit accesses, a bank conflict only occurs if two threads in either of the halfwarps access different addresses belonging to the same bank.
Unlike for devices of compute capability 1.x, there are no bank conflicts for arrays
of doubles accessed as follows, for example:
extern __shared__ float shared[];
double data = shared[BaseIndex + tid];
128-Bit Accesses
The majority of 128-bit accesses will cause 2-way bank conflicts, even if no two
threads in a quarter-warp access different addresses belonging to the same bank.
Therefore, to determine the ways of bank conflicts, one must add 1 to the
maximum number of threads in a quarter-warp that access different addresses
belonging to the same bank.
F.4.4
Constant Memory
In addition to the constant memory space supported by devices of all compute
capabilities (where __constant__ variables reside), devices of compute
capability 2.x support the LDU (LoaD Uniform) instruction that the compiler uses
to load any variable that is:
pointing to global memory,
read-only in the kernel (programmer can enforce this using the const
keyword),
not dependent on thread ID.
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F.5
Compute Capability 3.0
F.5.1
Architecture
Compute Capabilities
A multiprocessor consists of:
192 CUDA cores for arithmetic operations (see Section 5.4.1 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, both of which are used to cache accesses to local or global
memory, including temporary register spills. The cache behavior (e.g. whether reads
are cached in both L1 and L2 or in L2 only) can be partially configured on a peraccess basis using modifiers to the load or store instruction.
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 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
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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.
Applications may query the L2 cache size by checking the l2CacheSize device
property (see Section 3.2.6.1). The maximum L2 cache size is 512 KB.
Multiprocessors are grouped into Graphics Processor Clusters (GPCs). A GPC includes
two multiprocessors.
Each multiprocessor has a read-only texture cache to speed up reads from the
texture memory space, which resides in device memory. It accesses the texture cache
via a texture unit that implements the various addressing modes and data filtering
mentioned in Section 3.2.10.
F.5.2
Global Memory
Global memory accesses for devices of compute capability 3.0 behave in the same
way as for devices of compute capability 2.x (see Section F.4.2).
Figure F-1 shows some examples of global memory accesses and corresponding
memory transactions based on compute capability.
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Aligned and sequential
Addresses:
96
128
Threads:
160
192
224
…
0
Compute capability:
1.0 and 1.1
Memory transactions:
256
31
1.2 and 1.3
2.x and 3.0
Uncached
1 x 64B at 128
1 x 64B at 192
288
Cached
1 x 64B at 128
1 x 64B at 192
1 x 128B at 128
Aligned and non-sequential
Addresses:
96
128
Threads:
160
192
224
…
0
1.0 and 1.1
Compute capability:
Memory transactions:
256
31
1.2 and 1.3
2.x and 3.0
Uncached
8x
8x
8x
8x
32B at 128
32B at 160
32B at 192
32B at 224
288
Cached
1 x 64B at 128
1 x 64B at 192
1 x 128B at 128
Misaligned and sequential
Addresses:
96
128
Threads:
Compute capability:
160
192
224
…
0
1.0 and 1.1
Memory transactions:
1.2 and 1.3
Uncached
7x
8x
8x
8x
1x
32B at 128
32B at 160
32B at 192
32B at 224
32B at 256
1 x 128B at 128
1 x 64B at 192
1 x 32B at 256
256
288
31
2.x and 3.0
Cached
1 x 128B at 128
1 x 128B at 256
Figure F-1. Examples of Global Memory Accesses by a Warp,
4-Byte Word per Thread, and Associated Memory
Transactions Based on Compute Capability
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F.5.3
Compute Capabilities
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 F-2 shows some examples of strided access.
Figure F-3 shows some examples of memory read accesses that involve the
broadcast mechanism.
F.5.3.1
64-Bit Mode
Successive 64-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 address within the same 64-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).
In this mode, the same access pattern generates fewer bank conflicts than on
devices of compute capability 2.x for 64-bit accesses and as many or fewer for 32-bit
accesses.
F.5.3.2
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 address 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 two
addresses 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 address is written by
only one of the threads (which thread performs the write is undefined).
In this mode, the same access pattern generates as many or fewer bank conflicts
than on devices of compute capability 2.x.
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Appendix F.
Threads:
Banks:
Threads:
Banks:
Compute Capabilities
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Left: Linear addressing with a stride of one 32-bit word (no bank conflict).
Middle: Linear addressing with a stride of two 32-bit words (no bank conflict).
Right: Linear addressing with a stride of three 32-bit words (no bank conflict).
Figure F-2
Examples of Strided Shared Memory Accesses for
Devices of Compute Capability 3.0
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Appendix F.
Compute Capabilities
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Left: Conflict-free access via random permutation.
Middle: Conflict-free access since threads 3, 4, 6, 7, and 9 access the same word within bank 5.
Right: Conflict-free broadcast access (threads access the same word within a bank).
Figure F-3
154
Examples of Irregular Shared Memory Accesses
for Devices of Compute Capability 3.0
CUDA C Programming Guide Version 4.2
Appendix G.
Driver API
This appendix assumes knowledge of the concepts described in Section 3.2.
The driver API is implemented in the nvcuda dynamic library 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 G-1.
Table G-1. 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
Section G.1.
Within a CUDA context, kernels are explicitly loaded as PTX or binary objects by
the host code as described in Section G.2. Kernels written in C must therefore be
compiled separately into PTX or binary objects. Kernels are launched using API
entry points as described in Section G.3.
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
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Appendix G.
Driver API
incompatible with future architectures, whereas PTX code is compiled to binary
code at load time by the device driver.
Here is the host code of the sample from Section 2.1 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
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Appendix G.
Driver API
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 SDK code sample.
G.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 G-1.
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Appendix G.
Driver API
Library Initialization Call
cuCtxCreate()
Initialize
context
cuCtxPopCurrent()
Library Call
cuCtxPushCurrent()
Use
context
cuCtxPopCurrent()
Figure G-1 Library Context Management
G.2
Module
Modules are dynamically loadable packages of device code and data, akin to DLLs in
Windows, that are output by nvcc (see Section 3.1). 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 ERROR_BUFFER_SIZE 100
CUmodule cuModule;
CUjit_option options[3];
void* values[3];
char* PTXCode = "some PTX code";
options[0] = CU_ASM_ERROR_LOG_BUFFER;
values[0] = (void*)malloc(ERROR_BUFFER_SIZE);
options[1] = CU_ASM_ERROR_LOG_BUFFER_SIZE_BYTES;
values[1] = (void*)ERROR_BUFFER_SIZE;
options[2] = CU_ASM_TARGET_FROM_CUCONTEXT;
values[2] = 0;
cuModuleLoadDataEx(&cuModule, PTXCode, 3, options, values);
for (int i = 0; i < values[1]; ++i) {
// Parse error string here
}
G.3
Kernel Execution
cuLaunchKernel() launches a kernel with a given execution configuration.
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Appendix G.
Driver API
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.
Alignment requirements in device code for the built-in vector types are listed in
Table B-1. 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-aligndouble) 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,
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Appendix G.
Driver API
CU_LAUNCH_PARAM_BUFFER_SIZE,
CU_LAUNCH_PARAM_END
¶mBufferSize,
};
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 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;
G.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 Section 3.2),
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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