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 80
Сторінка viii
... Datasets................................................................................................ 209 Using ballot ... Dataset size..................................................................................................
... Datasets................................................................................................ 209 Using ballot ... Dataset size..................................................................................................
Сторінка 1
... datasets, the industry produces ever-faster computers. It is through some of these evolutions that GPU CUDA technology has come about today. Both supercomputers and desktop computing are moving toward a heterogeneous computing ...
... datasets, the industry produces ever-faster computers. It is through some of these evolutions that GPU CUDA technology has come about today. Both supercomputers and desktop computing are moving toward a heterogeneous computing ...
Сторінка 4
... dataset usually cannot, so the processor, despite all this cache trickery, is quite often limited by the memory throughput or bandwidth. When the processor fetches an instruction or data item from the cache instead of the main memory ...
... dataset usually cannot, so the processor, despite all this cache trickery, is quite often limited by the memory throughput or bandwidth. When the processor fetches an instruction or data item from the cache instead of the main memory ...
Сторінка 6
... dataset, there were 64 K processors doing this task. Let's take the simple example of manipulating the color of an RGB (red, green, blue) image. Each color is made up of a single byte, with 3 bytes representing the color of a single ...
... dataset, there were 64 K processors doing this task. Let's take the simple example of manipulating the color of an RGB (red, green, blue) image. Each color is made up of a single byte, with 3 bytes representing the color of a single ...
Сторінка 8
... datasets managed by the regular processor. The Cell is a particularly interesting processor for us, as it's a similar design to what NVIDIA later used in the G80 and subsequent GPUs. Sony also used it in their PS3 console machines in ...
... datasets managed by the regular processor. The Cell is a particularly interesting processor for us, as it's a similar design to what NVIDIA later used in the G80 and subsequent GPUs. Sony also used it in their PS3 console machines in ...
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