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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З цієї книги
Результати 6-10 із 88
Сторінка 79
... elements to 256 elements, you will access and write beyond the end of the array. This array out-of-bounds error will not be caught by the compiler and the code may actually run, depending on what is located after the destination array ...
... elements to 256 elements, you will access and write beyond the end of the array. This array out-of-bounds error will not be caught by the compiler and the code may actually run, depending on what is located after the destination array ...
Сторінка 84
... Element 8 X = 3 Y = 1 Array Element 9 X = 4 Y = 1 Array Element 10 Array Element 11 Array Element 12 Array Element 13 Array Element 14 X = 0 Y = 2 X = 1 Y = 2 X = 2 Y = 2 X = 3 Y = 2 X = 0 Y = 2 FIGURE 5.10 Array mapping to elements ...
... Element 8 X = 3 Y = 1 Array Element 9 X = 4 Y = 1 Array Element 10 Array Element 11 Array Element 12 Array Element 13 Array Element 14 X = 0 Y = 2 X = 1 Y = 2 X = 2 Y = 2 X = 3 Y = 2 X = 0 Y = 2 FIGURE 5.10 Array mapping to elements ...
Сторінка 87
... Element 1 X = 1 Y = 0 Array Element 2 X = 2 Y = 0 Array Element 3 X = 3 Y = 0 Array Element 4 X = 4 Y = 0 Grids 87 Array Element 5 X = 0 Y = 1 Array Element 6 X = 1 Y= 1 Array Element 7 X = 2 Y = 1 Array Element 8 X = 3 Y = 1 Array ...
... Element 1 X = 1 Y = 0 Array Element 2 X = 2 Y = 0 Array Element 3 X = 3 Y = 0 Array Element 4 X = 4 Y = 0 Grids 87 Array Element 5 X = 0 Y = 1 Array Element 6 X = 1 Y= 1 Array Element 7 X = 2 Y = 1 Array Element 8 X = 3 Y = 1 Array ...
Сторінка 97
... element in the array is 0, you increment bin 0. If the value of the element is 10, you increment bin 10, etc. The algorithm from a serial perspective is quite simple: for (unsigned int i1⁄40; i< max; iþþ) { bin[array[i]]þþ; } Here you ...
... element in the array is 0, you increment bin 0. If the value of the element is 10, you increment bin 10, etc. The algorithm from a serial perspective is quite simple: for (unsigned int i1⁄40; i< max; iþþ) { bin[array[i]]þþ; } Here you ...
Сторінка 98
... elements contending for any single bin is simply the array size in elements divided by the number of bins. With a 512 MB array (524,288 elements) you would have 131,072 elements contending for each bin. In the worst case, all elements ...
... elements contending for any single bin is simply the array size in elements divided by the number of bins. With a 512 MB array (524,288 elements) you would have 131,072 elements contending for each bin. In the worst case, all elements ...
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