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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... functions.............................................................. 532 Device, global, and host functions ... Atomic operations........................................................................................... 541 ...
... functions.............................................................. 532 Device, global, and host functions ... Atomic operations........................................................................................... 541 ...
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... operate cooperatively in batches called warps. We will look at this further in the coming chapters. CONCURRENCY. The first ... atomic operations to or from global memory. CUDA splits problems into grids of blocks, each containing multiple ...
... operate cooperatively in batches called warps. We will look at this further in the coming chapters. CONCURRENCY. The first ... atomic operations to or from global memory. CUDA splits problems into grids of blocks, each containing multiple ...
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... atomic operations and synchronization primitives that communicate data between threads in addition to simply synchronizing. We look at some examples of this later in the text. Tiling/grids The approach CUDA uses with all problems is to ...
... atomic operations and synchronization primitives that communicate data between threads in addition to simply synchronizing. We look at some examples of this later in the text. Tiling/grids The approach CUDA uses with all problems is to ...
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... atomic operations. Atomic operations are those where we can guarantee a complete operation without any other thread interrupting. In effect, the hardware implements a barrier point at the entry of the atomic function and guarantees the ...
... atomic operations. Atomic operations are those where we can guarantee a complete operation without any other thread interrupting. In effect, the hardware implements a barrier point at the entry of the atomic function and guarantees the ...
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... atomic operations. Compared to having to go out to the global memory on the GPU, using the shared cache is an order of magnitude faster. Support for ECC memory is a must for data centers. ECC memory provides for automatic error ...
... atomic operations. Compared to having to go out to the global memory on the GPU, using the shared cache is an order of magnitude faster. Support for ECC memory is a must for data centers. ECC memory provides for automatic error ...
Зміст
1 | |
21 | |
37 | |
53 | |
69 | |
Chapter 6 Memory Handling with CUDA | 107 |
Chapter 7 Using CUDA in Practice | 203 |
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