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
... .................................................. 305 Strategy 1: Parallel/Serial GPU/CPU Problem Breakdown .................................. 305 Analyzing the problem............................................................
... .................................................. 305 Strategy 1: Parallel/Serial GPU/CPU Problem Breakdown .................................. 305 Analyzing the problem............................................................
Сторінка 2
... problem with memory bandwidth when you consider the processor clock speed. If you take a processor running at 4 GHz, you need to potentially fetch, every cycle, an instruction (an operator) plus some data (an operand). Each instruction ...
... problem with memory bandwidth when you consider the processor clock speed. If you take a processor running at 4 GHz, you need to potentially fetch, every cycle, an instruction (an operator) plus some data (an operand). Each instruction ...
Сторінка 10
... problem with cluster computing is it's only as fast as the amount of internode communication that is necessary for the problem. If you have 32 nodes and the problem breaks down into 32 nice chunks and requires no internode communication ...
... problem with cluster computing is it's only as fast as the amount of internode communication that is necessary for the problem. If you have 32 nodes and the problem breaks down into 32 nice chunks and requires no internode communication ...
Сторінка 18
... problem into N parts, according to the number of available processor cores. OpenMP support is built into many compilers, including the NVCC compiler used for CUDA. OpenMP tends to hit problems with scaling due to the underlying CPU ...
... problem into N parts, according to the number of available processor cores. OpenMP support is built into many compilers, including the NVCC compiler used for CUDA. OpenMP tends to hit problems with scaling due to the underlying CPU ...
Сторінка 23
... problem. All threads requested a lock, waited, and updated the shared resource. Everything worked fine and the ... Problems 23 SERIAL/PARALLEL PROBLEMS.
... problem. All threads requested a lock, waited, and updated the shared resource. Everything worked fine and the ... Problems 23 SERIAL/PARALLEL PROBLEMS.
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