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 83
Сторінка viii
... streaming version ............................................................................... 249 AES in ... streams...................................................................................... 263 AES summary ..........
... streaming version ............................................................................... 249 AES in ... streams...................................................................................... 263 AES summary ..........
Сторінка 3
... stream as a literal value. The computer would then iterate around the same instructions, over and over again 100 times. For each value calculated, we have control, memory, and calculation instructions, fetched and executed. This is ...
... stream as a literal value. The computer would then iterate around the same instructions, over and over again 100 times. For each value calculated, we have control, memory, and calculation instructions, fetched and executed. This is ...
Сторінка 6
... stream on each loop iteration. The Connection Machine used something called SIMD (single instruction, multiple data), which is used today in modern processors and known by names such as SSE (Streaming SIMD Extensions), MMX (Multi-Media ...
... stream on each loop iteration. The Connection Machine used something called SIMD (single instruction, multiple data), which is used today in modern processors and known by names such as SSE (Streaming SIMD Extensions), MMX (Multi-Media ...
Сторінка 7
... SPE SPE SPE SPE L L L L E M M M M R E E E E M M M M O O O O R R R R Y Y Y Y FIGURE 1.4 IBM cell processor die layout (8 SPE version). processor, connected to a number of high-speed stream processors. The. Cell Processor 7 CELL PROCESSOR.
... SPE SPE SPE SPE L L L L E M M M M R E E E E M M M M O O O O R R R R Y Y Y Y FIGURE 1.4 IBM cell processor die layout (8 SPE version). processor, connected to a number of high-speed stream processors. The. Cell Processor 7 CELL PROCESSOR.
Сторінка 8
... stream processors and the outside world. The stream SIMD processors, or SPEs as IBM called them, would process datasets managed by the regular processor. The Cell is a particularly interesting processor for us, as it's a similar design ...
... stream processors and the outside world. The stream SIMD processors, or SPEs as IBM called them, would process datasets managed by the regular processor. The Cell is a particularly interesting processor for us, as it's a similar design ...
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