CUDA Programming: A Developer's Guide to Parallel Computing with GPUsNewnes, 28 груд. 2012 р. - 600 стор. If you need to learn CUDA but don't have experience with parallel computing, CUDA Programming: A Developer's Introduction offers a detailed guide to CUDA with a grounding in parallel fundamentals. It starts by introducing CUDA and bringing you up to speed on GPU parallelism and hardware, then delving into CUDA installation. Chapters on core concepts including threads, blocks, grids, and memory focus on both parallel and CUDA-specific issues. Later, the book demonstrates CUDA in practice for optimizing applications, adjusting to new hardware, and solving common problems.
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Сторінка viii
... ....................................... 302 Optimizing Your Application...................................................... 305 Strategy 1: Parallel/Serial GPU/CPU Problem Breakdown ...............................
... ....................................... 302 Optimizing Your Application...................................................... 305 Strategy 1: Parallel/Serial GPU/CPU Problem Breakdown ...............................
Сторінка ix
... optimizations............................................................ 369 Divergence..................................... ... Optimization................................................................................ 439 Answers ...
... optimizations............................................................ 369 Divergence..................................... ... Optimization................................................................................ 439 Answers ...
Сторінка xiv
... Optimizing Your Application. A detailed breakdown of the main areas that limit performance in CUDA. We look at the tools and techniques that are available for analysis of CUDA code. Chapter 10: Libraries and SDK. A look at some of the ...
... Optimizing Your Application. A detailed breakdown of the main areas that limit performance in CUDA. We look at the tools and techniques that are available for analysis of CUDA code. Chapter 10: Libraries and SDK. A look at some of the ...
Сторінка 47
... optimization called double buffering, which works as shown in Figure 3.7. To use this method we require double the memory space we'd normally use, which may well be an issue if your target market only had a 512 MB card. However, with ...
... optimization called double buffering, which works as shown in Figure 3.7. To use this method we require double the memory space we'd normally use, which may well be an issue if your target market only had a 512 MB card. However, with ...
Сторінка 91
... optimization. AVX is now supported by the current GNU gcc compiler. Microsoft Visual Studio 2010 supports it through the use of a “/arch:AVX” compiler switch. Given this lack of support until relatively recently, vector-type ...
... optimization. AVX is now supported by the current GNU gcc compiler. Microsoft Visual Studio 2010 supports it through the use of a “/arch:AVX” compiler switch. Given this lack of support until relatively recently, vector-type ...
Зміст
Chapter 8 MultiCPU and MultiGPU Solutions | 267 |
Chapter 9 Optimizing Your Application | 305 |
Chapter 10 Libraries and SDK | 441 |
Chapter 11 Designing GPUBased Systems | 503 |
Chapter 12 Common Problems Causes and Solutions | 527 |
Index | 565 |
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CUDA Programming: A Developer's Guide to Parallel Computing with GPUs Shane Cook Обмежений попередній перегляд - 2012 |
Загальні терміни та фрази
256 threads algorithm allocate application array atomic atomic operations blockDim.x blockIdx.x bytes calculation compiler compute 2.x const int const u32 constant memory copy CUDA CALL cuda CUDA cores dataset device device_num elements example execution Fermi Figure function GB/s GeForce GTX 470:GMEM global memory GMEM hardware host memory ID:0 GeForce GTX InfiniBand instruction issue iterations Kepler kernel L1 cache latency Linux look loop malloc Memcpy memory access memory bandwidth memory fetch merge sort node num_elem num_elements num_threads number of blocks number of threads NVIDIA OpenMP operation optimization output Parallel Nsight parameter PCI-E performance pointer prefix sum problem processor radix sort reduce registers result serial shared memory SIMD simply single SP SP SP speedup stream synchronization Tesla threadIdx.x threads per block transfer typically uint4 unsigned int usage version is faster void warp write þ¼