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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... example earlier, the PPC core fetches a chunk of data to work on. It allocates these to the eight SPEs. As we do the same thing in each SPE, each SPE fetches the byte, decrements it, and writes its bit back to its local memory. When all ...
... example earlier, the PPC core fetches a chunk of data to work on. It allocates these to the eight SPEs. As we do the same thing in each SPE, each SPE fetches the byte, decrements it, and writes its bit back to its local memory. When all ...
Сторінка 14
... examples, and tools to help with development are available from its website athttp://www.nvidia.com under CudaZone ... example, making a DVD from your home movies (video transcoding)dwe see most mainstream video packages now supporting ...
... examples, and tools to help with development are available from its website athttp://www.nvidia.com under CudaZone ... example, making a DVD from your home movies (video transcoding)dwe see most mainstream video packages now supporting ...
Сторінка 24
... example, those that iterate over a large number of steps. For these, consider each step oriteration individually. Can the data points for the step be represented as a transformation of the input dataset? If so, then you simply have a ...
... example, those that iterate over a large number of steps. For these, consider each step oriteration individually. Can the data points for the step be represented as a transformation of the input dataset? If so, then you simply have a ...
Сторінка 30
... example. Thus, we could increase this number to eight and we might see an increase in performance. However, at some point, sometimes even at less than the number of cores, the CPU hits a point where there are just too many threads. At ...
... example. Thus, we could increase this number to eight and we might see an increase in performance. However, at some point, sometimes even at less than the number of cores, the CPU hits a point where there are just too many threads. At ...
Сторінка 31
... example. This can be easily implemented as SIMD instructions. In effect, you are programming “for this range of data, perform this operation” instead of “for this data point, perform this operation.” As the data operation or ...
... example. This can be easily implemented as SIMD instructions. In effect, you are programming “for this range of data, perform this operation” instead of “for this data point, perform this operation.” As the data operation or ...
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