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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... cores to processors, rather than continuously trying to increase CPU clock rates ... threads all executing at once. Now the programmer has to think about two ... 256 CUDA core device, and the Fermi is a 512 CUDA core device. We see NVIDIA ...
... cores to processors, rather than continuously trying to increase CPU clock rates ... threads all executing at once. Now the programmer has to think about two ... 256 CUDA core device, and the Fermi is a 512 CUDA core device. We see NVIDIA ...
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... threads, yet there are six data items in the queue. Why not fork six threads? The reality is that in most problems ... 256 threads can be used. You can launch 256 threads and leave most of them idle until such time as needed. Such idle ...
... threads, yet there are six data items in the queue. Why not fork six threads? The reality is that in most problems ... 256 threads can be used. You can launch 256 threads and leave most of them idle until such time as needed. Such idle ...
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... threads, typically 256 or 512 threads per block. A number of SMs exist on a single GPU and share a common global memory space. Together as a single GPU they can operate at peak speeds of up to 3 teraflops/s (GTX680). While peak ...
... threads, typically 256 or 512 threads per block. A number of SMs exist on a single GPU and share a common global memory space. Together as a single GPU they can operate at peak speeds of up to 3 teraflops/s (GTX680). While peak ...
Сторінка 79
... 256 threads in total and index the array from 0 to 255. If you don't also change the size of the array, from 128 elements to 256 elements, you will access and write beyond the end of the array. This array out-of-bounds error will not be ...
... 256 threads in total and index the array from 0 to 255. If you don't also change the size of the array, from 128 elements to 256 elements, you will access and write beyond the end of the array. This array out-of-bounds error will not be ...
Сторінка 91
... threads. Warps are the basic unit of execution on the GPU. The GPU is ... 256 bits. This is quite powerful, but until recently, unless you were using ... threads. You can actually write a single-thread GPU program with a simple if ...
... threads. Warps are the basic unit of execution on the GPU. The GPU is ... 256 bits. This is quite powerful, but until recently, unless you were using ... threads. You can actually write a single-thread GPU program with a simple if ...
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