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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Сторінка 6
... single byte, with 3 bytes representing the color of a single pixel. Let's suppose we want to reduce the blue level to zero. Let's assume the memory is configured in three banks of red, blue, and green, rather than being interleaved ...
... single byte, with 3 bytes representing the color of a single pixel. Let's suppose we want to reduce the blue level to zero. Let's assume the memory is configured in three banks of red, blue, and green, rather than being interleaved ...
Сторінка 9
... single machine, costs rapidly increase. While a 2.6 GHz processor may cost you $250 USD, the same processor at 3.4 GHz may be $1400 for less than a 1 GHz increase in clock speed. A similar relationship is seen for both speed and size ...
... single machine, costs rapidly increase. While a 2.6 GHz processor may cost you $250 USD, the same processor at 3.4 GHz may be $1400 for less than a 1 GHz increase in clock speed. A similar relationship is seen for both speed and size ...
Сторінка 12
... SINGLE-CORE. SOLUTION. One of the problems with today's modern processors is they have hit a clock rate limit at around 4 GHz. At this point they just generate too much heat for the current technology and require special and expensive ...
... SINGLE-CORE. SOLUTION. One of the problems with today's modern processors is they have hit a clock rate limit at around 4 GHz. At this point they just generate too much heat for the current technology and require special and expensive ...
Сторінка 13
... single-thread approach, to dealing with multiple threads all executing at once. Now the programmer has to think about two, four, six, or eight program threads and how they interact and communicate with one another. When dual-core CPUs ...
... single-thread approach, to dealing with multiple threads all executing at once. Now the programmer has to think about two, four, six, or eight program threads and how they interact and communicate with one another. When dual-core CPUs ...
Сторінка 15
... single-precision (32-bit) floating-point performance, not double-precision (64-bit) precision. Also be careful with the GF100 (Fermi) series, as the Tesla variant has double the number of double-precision units found in the standard ...
... single-precision (32-bit) floating-point performance, not double-precision (64-bit) precision. Also be careful with the GF100 (Fermi) series, as the Tesla variant has double the number of double-precision units found in the standard ...
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