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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... write cycles compared with fetching and writing 1 byte at a time. AVX extends this to 256 bits, making it even more effective. For a high-definition (HD) video image of 1920 Â 1080 resolution, the data size is 2,073,600 bytes, or around ...
... write cycles compared with fetching and writing 1 byte at a time. AVX extends this to 256 bits, making it even more effective. For a high-definition (HD) video image of 1920 Â 1080 resolution, the data size is 2,073,600 bytes, or around ...
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... write a program to execute on the PowerPC core processor. It then invokes a program, using an entirely different ... writes its bit back to its local memory. When all SPEs are done, the PC core fetches the data from each SPE. It then ...
... write a program to execute on the PowerPC core processor. It then invokes a program, using an entirely different ... writes its bit back to its local memory. When all SPEs are done, the PC core fetches the data from each SPE. It then ...
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... write the old data and one to get the new data. The big advantage of the programmer-controlled on-chip memory is that the programmeris in control of when the writes happen. If you are performing some local transformation of the data ...
... write the old data and one to get the new data. The big advantage of the programmer-controlled on-chip memory is that the programmeris in control of when the writes happen. If you are performing some local transformation of the data ...
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... write data back to memory. Interleaving the data elements by core means the cache has to coordinate and combine the writes from different cores, which is usually a bad idea. If the algorithm permits, we can exploit a certain type of ...
... write data back to memory. Interleaving the data elements by core means the cache has to coordinate and combine the writes from different cores, which is usually a bad idea. If the algorithm permits, we can exploit a certain type of ...
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... writes adjacent memory locations as each write forces a global update to every core's cache. A non cache-coherent system by comparison does not automatically update the other core's caches. It relies on the programmer to write the ...
... writes adjacent memory locations as each write forces a global update to every core's cache. A non cache-coherent system by comparison does not automatically update the other core's caches. It relies on the programmer to write the ...
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