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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Результати 6-10 із 67
Сторінка 30
... output of each processor core to separate areas/addresses. This supports the view of a program where a single core is responsible for a single or small set of outputs. CPUs follow the cache-coherent approach whereas the GPU does not and ...
... output of each processor core to separate areas/addresses. This supports the view of a program where a single core is responsible for a single or small set of outputs. CPUs follow the cache-coherent approach whereas the GPU does not and ...
Сторінка 35
... output data item. If, however, we can fill the GPU with threads on this basis and there is still more data that could be processed, can we still improve the throughput? The answer is yes, but only through the use of ILP. ILP exploits ...
... output data item. If, however, we can fill the GPU with threads on this basis and there is still more data that could be processed, can we still improve the throughput? The answer is yes, but only through the use of ILP. ILP exploits ...
Сторінка 39
... output (I/O) performance with loading and saving data. The other major advance with the Sandybridge design was the introduction of the AVX (Advanced Vector Extensions) instruction set, also supported by AMD processors. AVX allows for ...
... output (I/O) performance with loading and saving data. The other major advance with the Sandybridge design was the introduction of the AVX (Advanced Vector Extensions) instruction set, also supported by AMD processors. AVX allows for ...
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... output, you'll need an OpenGL development environment. Install this with the following command: sudo yum install freeglut-devel libXi-devel libXmu-devel Now you're ready to install the CUDA drivers. Make sure you install at least ...
... output, you'll need an OpenGL development environment. Install this with the following command: sudo yum install freeglut-devel libXi-devel libXmu-devel Now you're ready to install the CUDA drivers. Make sure you install at least ...
Сторінка 63
... outputs, you can simply run two monitors off the display card should you have a dual-monitor setup. Note in the latest release, 2.2, the need for two GPUs was dropped. It's also possible to set up the tool to acquire data from a remote ...
... outputs, you can simply run two monitors off the display card should you have a dual-monitor setup. Note in the latest release, 2.2, the need for two GPUs was dropped. It's also possible to set up the tool to acquire data from a remote ...
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