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 6
... SIMD (single instruction, multiple data), which is used today in modern processors and known by names such as SSE (Streaming SIMD Extensions), MMX (Multi-Media eXtension), and AVX (Advanced Vector eXtensions). The concept is to define a ...
... SIMD (single instruction, multiple data), which is used today in modern processors and known by names such as SSE (Streaming SIMD Extensions), MMX (Multi-Media eXtension), and AVX (Advanced Vector eXtensions). The concept is to define a ...
Сторінка 7
... SIMD instruction set extends this to 128 bits. With SIMD instructions on such a processor, we eliminate all redundant instruction memory fetches, and generate one sixteenth of the memory read and write cycles compared with fetching and ...
... SIMD instruction set extends this to 128 bits. With SIMD instructions on such a processor, we eliminate all redundant instruction memory fetches, and generate one sixteenth of the memory read and write cycles compared with fetching and ...
Сторінка 8
... SIMD processors, or SPEs as IBM called them, would process datasets managed by the regular processor. The Cell is a particularly interesting processor for us, as it's a similar design to what NVIDIA later used in the G80 and subsequent ...
... SIMD processors, or SPEs as IBM called them, would process datasets managed by the regular processor. The Cell is a particularly interesting processor for us, as it's a similar design to what NVIDIA later used in the G80 and subsequent ...
Сторінка 15
... SIMD SPE cores. Each GPU device contains a set of SMs, each of which contain a set of SPs or CUDA cores. The SPs execute work as parallel sets of up to 32 units. They eliminate a lot of the complex circuitry needed on CPUs to achieve ...
... SIMD SPE cores. Each GPU device contains a set of SMs, each of which contain a set of SPs or CUDA cores. The SPs execute work as parallel sets of up to 32 units. They eliminate a lot of the complex circuitry needed on CPUs to achieve ...
Сторінка 29
... SIMD (single instruction, multiple data) model. This would make use of special SIMD instructions such as MMX, SSE, AVX, etc. present in many x86-based CPUs. Thus, thread 0 could actually fetch multiple adjacent elements and process them ...
... SIMD (single instruction, multiple data) model. This would make use of special SIMD instructions such as MMX, SSE, AVX, etc. present in many x86-based CPUs. Thus, thread 0 could actually fetch multiple adjacent elements and process them ...
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