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 36
... Parallel Nsight and CUDA-GDB can detect such stack overflow issues. In selecting a recursive algorithm be aware that you are making a tradeoff of development time versus performance. It may be easier to conceptualize and therefore code ...
... Parallel Nsight and CUDA-GDB can detect such stack overflow issues. In selecting a recursive algorithm be aware that you are making a tradeoff of development time versus performance. It may be easier to conceptualize and therefore code ...
Сторінка 53
... Parallel Nsight debugger 1. 2. 3. 4. 5. CUDA Programming. http://dx.doi.org/10.1016/B978-0-12-415933-4.00004-1 Copyright Ó 2013 Elsevier Inc. All rights reserved. 53 FIGURE 4.1 “Folder Options” to see hidden files. Under Windows Chapter ...
... Parallel Nsight debugger 1. 2. 3. 4. 5. CUDA Programming. http://dx.doi.org/10.1016/B978-0-12-415933-4.00004-1 Copyright Ó 2013 Elsevier Inc. All rights reserved. 53 FIGURE 4.1 “Folder Options” to see hidden files. Under Windows Chapter ...
Сторінка 62
... Parallel Nsight on the Windows and Linux platforms. This provides support for debugging CPU and GPU code and highlights areas where things are working less than efficiently. It also helps tremendously when trying to debug multithreaded ...
... Parallel Nsight on the Windows and Linux platforms. This provides support for debugging CPU and GPU code and highlights areas where things are working less than efficiently. It also helps tremendously when trying to debug multithreaded ...
Сторінка 63
A Developer's Guide to Parallel Computing with GPUs Shane Cook. FIGURE 4.7 Nsight integrated into Microsoft Visual Studio. the same machine as the development environment. Parallel Nsight works best with two CUDA capable GPUs, a ...
A Developer's Guide to Parallel Computing with GPUs Shane Cook. FIGURE 4.7 Nsight integrated into Microsoft Visual Studio. the same machine as the development environment. Parallel Nsight works best with two CUDA capable GPUs, a ...
Сторінка 64
... Parallel Nsight will no longer warn you TDR is enabled. To use Parallel Nsight on a remote machine, simply install the monitor package only on the remote Windows PC. When you first run the monitor, it will warn you Windows Firewall has ...
... Parallel Nsight will no longer warn you TDR is enabled. To use Parallel Nsight on a remote machine, simply install the monitor package only on the remote Windows PC. When you first run the monitor, it will warn you Windows Firewall has ...
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