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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Результати 1-5 із 88
Сторінка vii
... Memory Handling with CUDA.................................................... 107 Introduction ... Shared Memory..................................................................................................
... Memory Handling with CUDA.................................................... 107 Introduction ... Shared Memory..................................................................................................
Сторінка 6
... shared memory architecture, split into banks. These were connected to one, two, or four processors. It led the way for the creation of today's server-based symmetrical multiprocessor (SMP) systems in which multiple CPUs shared the same ...
... shared memory architecture, split into banks. These were connected to one, two, or four processors. It led the way for the creation of today's server-based symmetrical multiprocessor (SMP) systems in which multiple CPUs shared the same ...
Сторінка 23
... shared resources. Typically, sharing done with a semaphore, which is simply a lock or token. Whoever has the token ... memory space. This can be both an advantage in terms of not having to formally exchange data via messages, and a ...
... shared resources. Typically, sharing done with a semaphore, which is simply a lock or token. Whoever has the token ... memory space. This can be both an advantage in terms of not having to formally exchange data via messages, and a ...
Сторінка 27
... memory loads into the on-chip memory before they are needed. This works well with both an explicit local memory model such as the GPU's shared memory as well as a CPU-based cache. In the shared memory case you tell the memory management ...
... memory loads into the on-chip memory before they are needed. This works well with both an explicit local memory model such as the GPU's shared memory as well as a CPU-based cache. In the shared memory case you tell the memory management ...
Сторінка 42
... Memory (global, constant, shared) • Streaming multiprocessors (SMs) • Streaming processors (SPs) The main thing to notice here is that a GPU is really an array of SMs, each of which has N cores (8 in G80 and GT200, 32–48 in Fermi, 8 ...
... Memory (global, constant, shared) • Streaming multiprocessors (SMs) • Streaming processors (SPs) The main thing to notice here is that a GPU is really an array of SMs, each of which has N cores (8 in G80 and GT200, 32–48 in Fermi, 8 ...
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