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 8
... allocates these to the eight SPEs. As we do the same thing in each SPE, each SPE fetches the byte, decrements it, and writes its bit back to its local memory. When all SPEs are done, the PC core fetches the data from each SPE. It then ...
... allocates these to the eight SPEs. As we do the same thing in each SPE, each SPE fetches the byte, decrements it, and writes its bit back to its local memory. When all SPEs are done, the PC core fetches the data from each SPE. It then ...
Сторінка 14
... allocate the kernels to whatever GPU hardware is present. We'll cover scheduling in detail later. Provided there is enough parallelism in the task, as the number of SMs in the GPU grows, so should the speed of the program. However ...
... allocate the kernels to whatever GPU hardware is present. We'll cover scheduling in detail later. Provided there is enough parallelism in the task, as the number of SMs in the GPU grows, so should the speed of the program. However ...
Сторінка 29
... allocate, at least initially, a combination of blocks and threads such that a single thread processed a single element of data. As with the CPU, there are benefits from processing multiple elements per thread. This is somewhat limited ...
... allocate, at least initially, a combination of blocks and threads such that a single thread processed a single element of data. As with the CPU, there are benefits from processing multiple elements per thread. This is somewhat limited ...
Сторінка 30
... processes that the OS will allocate to one of N CPU cores. Each thread/process has an independent stream of instructions, and thus the hardware contains all 30 CHAPTER 2 Understanding Parallelism with GPUs FLYNN'S TAXONOMY.
... processes that the OS will allocate to one of N CPU cores. Each thread/process has an independent stream of instructions, and thus the hardware contains all 30 CHAPTER 2 Understanding Parallelism with GPUs FLYNN'S TAXONOMY.
Сторінка 36
... allocate enough stack space, as with CPUs, will result in the program failing. Some debugging tools such as Parallel Nsight and CUDA-GDB can detect such stack overflow issues. In selecting a recursive algorithm be aware that you are ...
... allocate enough stack space, as with CPUs, will result in the program failing. Some debugging tools such as Parallel Nsight and CUDA-GDB can detect such stack overflow issues. In selecting a recursive algorithm be aware that you are ...
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