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 24
... warp), each commanded by a sergeant (See Figure 2.1). Grid Block N-1 Block N Block N+1 Warp N-1 Warp 24 CHAPTER 2 Understanding Parallelism with GPUs CONCURRENCY.
... warp), each commanded by a sergeant (See Figure 2.1). Grid Block N-1 Block N Block N+1 Warp N-1 Warp 24 CHAPTER 2 Understanding Parallelism with GPUs CONCURRENCY.
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... Warp N Warp N+1 Warp N-1 Warp N Warp N+1 Warp N-1 Warp N Warp N+1 FIGURE 2.1 GPU-based view of threads. To perform some action, central command (the kernel/host program) must provide some action plus some data. Each soldier (thread) ...
... Warp N Warp N+1 Warp N-1 Warp N Warp N+1 Warp N-1 Warp N Warp N+1 FIGURE 2.1 GPU-based view of threads. To perform some action, central command (the kernel/host program) must provide some action plus some data. Each soldier (thread) ...
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... warp dispatcher instead of the usual single-warp dispatcher. The x60 series cards have always had a very high penetration into the midrange games market, so if your application is targeted at the consumer market, it is important to be ...
... warp dispatcher instead of the usual single-warp dispatcher. The x60 series cards have always had a very high penetration into the midrange games market, so if your application is targeted at the consumer market, it is important to be ...
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... warp (32 threads) and a half warp (16 threads), something we'll return to later. Thus, the 128 threads translate into four groups of 32 threads. The first set all run together to extract the thread ID and then calculate the address in ...
... warp (32 threads) and a half warp (16 threads), something we'll return to later. Thus, the 128 threads translate into four groups of 32 threads. The first set all run together to extract the thread ID and then calculate the address in ...
Сторінка 75
... warp. In Figure 5.5, you can see that when warp 0 is suspended pending its memory access completing, warp 1 becomes the executing warp. The GPU continues in this manner until all warps have moved ... Warp 0 (Theads 0 to 31) Warp. Threads 75.
... warp. In Figure 5.5, you can see that when warp 0 is suspended pending its memory access completing, warp 1 becomes the executing warp. The GPU continues in this manner until all warps have moved ... Warp 0 (Theads 0 to 31) Warp. Threads 75.
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