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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З цієї книги
Результати 6-10 із 93
Сторінка 18
... single node. However, unlike OpenMP, the programmer is responsible for thread management and synchronization. This provides more flexibility and consequently better performance for well-written programs. ZeroMQ (0MQ) is also something ...
... single node. However, unlike OpenMP, the programmer is responsible for thread management and synchronization. This provides more flexibility and consequently better performance for well-written programs. ZeroMQ (0MQ) is also something ...
Сторінка 22
... single node and is designed for shared memory machines that contain multicore processors. It does not have any concept of anything outside a single node or box. Thus, you are limited to problems that fit within a single box in terms of ...
... single node and is designed for shared memory machines that contain multicore processors. It does not have any concept of anything outside a single node or box. Thus, you are limited to problems that fit within a single box in terms of ...
Сторінка 25
... single output data point. A typical GPU has on the order of 24 K active threads. On Fermi GPUs you can define 65,535 Â 65,535 Â 1536 threads in total, 24 K of which are active at any time. This is usually enough to cover most problems ...
... single output data point. A typical GPU has on the order of 24 K active threads. On Fermi GPUs you can define 65,535 Â 65,535 Â 1536 threads in total, 24 K of which are active at any time. This is usually enough to cover most problems ...
Сторінка 28
... single stream and separate streams executed concurrently. Second, multiple GPUs can work together directly through either passing data via the host or passing data via messages directly to one another over the PCI-E bus. This latter ...
... single stream and separate streams executed concurrently. Second, multiple GPUs can work together directly through either passing data via the host or passing data via messages directly to one another over the PCI-E bus. This latter ...
Сторінка 29
... single SIMD instruction. If we consider the same problem on the GPU, each array needs to have a separate transformation performed on it. This naturally maps such that one transformation equates to a single GPU kernel (or program). Each ...
... single SIMD instruction. If we consider the same problem on the GPU, each array needs to have a separate transformation performed on it. This naturally maps such that one transformation equates to a single GPU kernel (or program). Each ...
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