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 2
... cores in one device, or double that if you count its hyperthreading ability as a real processor. A DDR-3 triple-channel memory setup on a I7 Nehalem system would produce the theoretical bandwidth figures shown in Table 1.1. Depending on ...
... cores in one device, or double that if you count its hyperthreading ability as a real processor. A DDR-3 triple-channel memory setup on a I7 Nehalem system would produce the theoretical bandwidth figures shown in Table 1.1. Depending on ...
Сторінка 3
... Core L2 Cache L1 Instruction L1 Data Processor Core L2 Cache L1 Instruction L1 Data Processor Core L2 Cache L1 Instruction L1 Data Processor Core FIGURE 1.1 Typical modern CPU cache organization. If the data is not in the first level ...
... Core L2 Cache L1 Instruction L1 Data Processor Core L2 Cache L1 Instruction L1 Data Processor Core L2 Cache L1 Instruction L1 Data Processor Core FIGURE 1.1 Typical modern CPU cache organization. If the data is not in the first level ...
Сторінка 4
... core devices, with the faulty cores disabled. However, the effect of larger, progressively more inefficient caches ultimately results in higher costs to the end user. M i s c I O Core 1 Q P I 1 Shared L3 Cache Core 2 Core 4 Core 3 Q ...
... core devices, with the faulty cores disabled. However, the effect of larger, progressively more inefficient caches ultimately results in higher costs to the end user. M i s c I O Core 1 Q P I 1 Shared L3 Cache Core 2 Core 4 Core 3 Q ...
Сторінка 6
... core CPU, and then installed some 4096 of these devices in one machine. The concept was different. Instead of one fast ... cores, each executing SIMD instructions on its dataset. Processors such as the Intel I7 are 64-bit processors ...
... core CPU, and then installed some 4096 of these devices in one machine. The concept was different. Instead of one fast ... cores, each executing SIMD instructions on its dataset. Processors such as the Intel I7 are 64-bit processors ...
Сторінка 8
... core processor. It then invokes a program, using an entirely different binary, on each of the stream processing elements (SPEs). Each SPE is actually a core in itself. It can execute an independent program from its own local memory ...
... core processor. It then invokes a program, using an entirely different binary, on each of the stream processing elements (SPEs). Each SPE is actually a core in itself. It can execute an independent program from its own local memory ...
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