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 6-10 із 44
Сторінка 70
... synchronization and coordination. In the same way as you have macro (large-scale) and micro (small-scale) economics, you have coarse and fine-grained parallelism. However, you only really find fine-grained parallelism at the programmer ...
... synchronization and coordination. In the same way as you have macro (large-scale) and micro (small-scale) economics, you have coarse and fine-grained parallelism. However, you only really find fine-grained parallelism at the programmer ...
Сторінка 94
... synchronization points in the kernel. These are points where every thread must wait on every other thread to reach the same point, for example, when you're doing a staged read and all threads must do the read. Due to the nature of the ...
... synchronization points in the kernel. These are points where every thread must wait on every other thread to reach the same point, for example, when you're doing a staged read and all threads must do the read. Due to the nature of the ...
Сторінка 102
... synchronization points typically slows down the program, but can lead to a more uniform access pattern in memory. Table 5.4 Histogram Results Factor MB/s Total Blocks Blocks per 102 CHAPTER 5 Grids, Blocks, and Threads.
... synchronization points typically slows down the program, but can lead to a more uniform access pattern in memory. Table 5.4 Histogram Results Factor MB/s Total Blocks Blocks per 102 CHAPTER 5 Grids, Blocks, and Threads.
Сторінка 149
Досягнуто ліміту перегляду цієї книги.
Досягнуто ліміту перегляду цієї книги.
Сторінка 187
Досягнуто ліміту перегляду цієї книги.
Досягнуто ліміту перегляду цієї книги.
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