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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... ............... 555 Developing for Future GPUs............................................................................... 555 Kepler...................................................................................................
... ............... 555 Developing for Future GPUs............................................................................... 555 Kepler...................................................................................................
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... Kepler hardware. Literally anyone who can program in C or C++ can program with CUDA in a few hours given a little training. Getting from novice CUDA programmer, with a several times speedup to 10 times–plus speedup is what you should be ...
... Kepler hardware. Literally anyone who can program in C or C++ can program with CUDA in a few hours given a little training. Getting from novice CUDA programmer, with a several times speedup to 10 times–plus speedup is what you should be ...
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... Kepler K20, yet to be released, will also have significant double precision performance over and above its already released K10 cousin. Note also, although not shown here, as the generations have evolved, the power consumption, clock ...
... Kepler K20, yet to be released, will also have significant double precision performance over and above its already released K10 cousin. Note also, although not shown here, as the generations have evolved, the power consumption, clock ...
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... Kepler devices, we could also create four separate kernels, one to process each array and run it concurrently. A data-based decomposition would instead split the first array into four blocks and assign one CPU core or one GPUSM to each ...
... Kepler devices, we could also create four separate kernels, one to process each array and run it concurrently. A data-based decomposition would instead split the first array into four blocks and assign one CPU core or one GPUSM to each ...
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... Kepler). Thus, we'd need 8 Â 4 1⁄4 32 blocks to load the four SMs correctly. As we have four independent operations, we can launch four simultaneous kernels on Fermi hardware via the streams feature (see Chapter 8 on using multiple GPUs) ...
... Kepler). Thus, we'd need 8 Â 4 1⁄4 32 blocks to load the four SMs correctly. As we have four independent operations, we can launch four simultaneous kernels on Fermi hardware via the streams feature (see Chapter 8 on using multiple GPUs) ...
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