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 1-5 із 90
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... array[i] 1⁄4 i * 10;. void some_function { int array[100]; int i 1⁄4 0; *The actual achieved dispatch rate can be higher or lower than one, which we use here for simplicity. 2 CHAPTER 1 A Short History of Supercomputing VON NEUMANN ...
... array[i] 1⁄4 i * 10;. void some_function { int array[100]; int i 1⁄4 0; *The actual achieved dispatch rate can be higher or lower than one, which we use here for simplicity. 2 CHAPTER 1 A Short History of Supercomputing VON NEUMANN ...
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... { array[i] 1⁄4 i * 10; } } If you look at how the processor would typically implement this, you would see the address of array loaded into some memory access register. The parameter i would be loaded into another register. The loop exit ...
... { array[i] 1⁄4 i * 10; } } If you look at how the processor would typically implement this, you would see the address of array loaded into some memory access register. The parameter i would be loaded into another register. The loop exit ...
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... array to every block. On the newer Fermi and Kepler devices, we could also create four separate kernels, one to process each array and run it concurrently. A data-based decomposition would instead split the first array into four blocks ...
... array to every block. On the newer Fermi and Kepler devices, we could also create four separate kernels, one to process each array and run it concurrently. A data-based decomposition would instead split the first array into four blocks ...
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... arrays in parallel. As with the CPU, however, it would be more efficient to work on one array at a time as this would likely result in better cache utilization. Thus, on the GPU we need to ensure we always have enough blocks (typically ...
... arrays in parallel. As with the CPU, however, it would be more efficient to work on one array at a time as this would likely result in better cache utilization. Thus, on the GPU we need to ensure we always have enough blocks (typically ...
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... in the inner loop and the Y pixel axis in the outer loop. It's possible to flatten this loop by considering all pixels as a single-dimensional array and iterating over pixels as opposed to 32 CHAPTER 2 Understanding Parallelism with GPUs.
... in the inner loop and the Y pixel axis in the outer loop. It's possible to flatten this loop by considering all pixels as a single-dimensional array and iterating over pixels as opposed to 32 CHAPTER 2 Understanding Parallelism with GPUs.
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