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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... issue. In a completely unrelated section of the code a pointer was not initialized under certain conditions. Due to the way the program ran, some of the time, depending on the thread execution order, the pointer would point to our ...
... issue. In a completely unrelated section of the code a pointer was not initialized under certain conditions. Due to the way the program ran, some of the time, depending on the thread execution order, the pointer would point to our ...
Сторінка 26
... issue, regardless of whether the processor tries to hide it from the programmer or not. The denial that this is an issue is what leads to the huge amount of hardware necessary to deal with memory latency. The design of GPUs takes a ...
... issue, regardless of whether the processor tries to hide it from the programmer or not. The denial that this is an issue is what leads to the huge amount of hardware necessary to deal with memory latency. The design of GPUs takes a ...
Сторінка 27
... issues, either initially or sometimes at all. It's a perfectly valid approach to develop a program, prove the concept, and then deal with locality issues. To facilitate such an approach and to deal with the issues of algorithms that did ...
... issues, either initially or sometimes at all. It's a perfectly valid approach to develop a program, prove the concept, and then deal with locality issues. To facilitate such an approach and to deal with the issues of algorithms that did ...
Сторінка 28
... issues with a pipeline-based pattern is, like any production line, it can only run as fast as the slowest component ... issue applies to providing a speedup when using a single CPU/GPU combination. If we move 80% of the work off the CPU ...
... issues with a pipeline-based pattern is, like any production line, it can only run as fast as the slowest component ... issue applies to providing a speedup when using a single CPU/GPU combination. If we move 80% of the work off the CPU ...
Сторінка 34
... issue. See chapter 12 for more information on this. Within a block of threads on a GPU there are a number of methods to communication between threads and to coordinate a certain amount of problem growth or varying levels of concurrency ...
... issue. See chapter 12 for more information on this. Within a block of threads on a GPU there are a number of methods to communication between threads and to coordinate a certain amount of problem growth or varying levels of concurrency ...
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