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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... host and SPEs increases. IBM used a high-powered version of the Cell ... memory, storage space) needed on a single machine, costs rapidly increase ... memory, and storage capacity. Not only do costs scale as computing requirements scale ...
... host and SPEs increases. IBM used a high-powered version of the Cell ... memory, storage space) needed on a single machine, costs rapidly increase ... memory, and storage capacity. Not only do costs scale as computing requirements scale ...
Сторінка 10
... memory/L1 cache. This is connected to an L2 cache that acts as an inter-SM switch. Data can be held in global memory storage where it's then extracted and used by the host, or sent via the PCI-E switch directly to the memory on another ...
... memory/L1 cache. This is connected to an L2 cache that acts as an inter-SM switch. Data can be held in global memory storage where it's then extracted and used by the host, or sent via the PCI-E switch directly to the memory on another ...
Сторінка 11
... Host Memory / CPU FIGURE 1.7 GPUs compared to a cluster. applications run on many nodes, each of which may contain many processing elements including GPUs. Distributed applications may, but do not need to, run in a controlled ...
... Host Memory / CPU FIGURE 1.7 GPUs compared to a cluster. applications run on many nodes, each of which may contain many processing elements including GPUs. Distributed applications may, but do not need to, run in a controlled ...
Сторінка 34
... memory space. In Windows a thread can come with a 1 MB stack allocation, meaning we'd rapidly run out of memory ... host program. Thus, such algorithms on the GPU side are typically implemented as a series of GPU kernel launches, each of ...
... memory space. In Windows a thread can come with a 1 MB stack allocation, meaning we'd rapidly run out of memory ... host program. Thus, such algorithms on the GPU side are typically implemented as a series of GPU kernel launches, each of ...
Сторінка 42
... memory on their top-end systems and dual-channel memory on the lower-end systems. AMD uses only dual-channel memory, leading to significantly less CPU host-memory bandwidth being available (Figure 3.4). One significant advantage of the ...
... memory on their top-end systems and dual-channel memory on the lower-end systems. AMD uses only dual-channel memory, leading to significantly less CPU host-memory bandwidth being available (Figure 3.4). One significant advantage of 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