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.
|
З цієї книги
Результати 1-5 із 47
Сторінка xiii
... speedup to 10 times–plus speedup is what you should be capable of by the end of this book. The book is very much aimed at learning CUDA, but with a focus on performance, having first achieved correctness. Your level of skill and ...
... speedup to 10 times–plus speedup is what you should be capable of by the end of this book. The book is very much aimed at learning CUDA, but with a focus on performance, having first achieved correctness. Your level of skill and ...
Сторінка 12
... speed up general-purpose computing. This led to the development of a number of initiatives (e.g., BrookGPU, Cg, CTM, etc.), all of which were aimed at making the GPU a real programmable device in the same way as the CPU. Unfortunately ...
... speed up general-purpose computing. This led to the development of a number of initiatives (e.g., BrookGPU, Cg, CTM, etc.), all of which were aimed at making the GPU a real programmable device in the same way as the CPU. Unfortunately ...
Сторінка 14
... speedup possible is limited by the amount of serial code. If you have an infinite amount of processing power and could do the parallel tasks in zero time, you would still be left with the time from the serial code part. Therefore, we ...
... speedup possible is limited by the amount of serial code. If you have an infinite amount of processing power and could do the parallel tasks in zero time, you would still be left with the time from the serial code part. Therefore, we ...
Сторінка 15
... speedups in scientific calculations. These cards can either be installed in a regular desktop PC or in dedicated server racks. NVIDIA provides such a system at http://www.nvidia.com/object/preconfigured_clusters.html, which claims to ...
... speedups in scientific calculations. These cards can either be installed in a regular desktop PC or in dedicated server racks. NVIDIA provides such a system at http://www.nvidia.com/object/preconfigured_clusters.html, which claims to ...
Сторінка 19
... speedup using such directives. It's a good solution for those programmers who need to get something working quickly ... speedups. Libraries like SDK provide Thrust, which provides common functions implemented in a very efficient way ...
... speedup using such directives. It's a good solution for those programmers who need to get something working quickly ... speedups. Libraries like SDK provide Thrust, which provides common functions implemented in a very efficient way ...
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 |
Інші видання - Показати все
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