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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... call depth and translate this into stack usage. The available stack can be queried with API call cudaDeviceGetLimit() . It can also be set with the API call cudaDeviceSetLimit(). Failure to allocate enough stack space, as with CPUs ...
... call depth and translate this into stack usage. The available stack can be queried with API call cudaDeviceGetLimit() . It can also be set with the API call cudaDeviceSetLimit(). Failure to allocate enough stack space, as with CPUs ...
Сторінка 47
... GPU memory space, the CPU fills the first buffer. Cycle 1: The CPU then invokes a CUDA kernel (a GPU task) on the GPU, which returns immediately to the CPU (a nonblocking call). The CPU then fetches the next data packet, from a disk ...
... GPU memory space, the CPU fills the first buffer. Cycle 1: The CPU then invokes a CUDA kernel (a GPU task) on the GPU, which returns immediately to the CPU (a nonblocking call). The CPU then fetches the next data packet, from a disk ...
Сторінка 67
... call must be checked and some handler written. This makes for very tiresome and highly indented programming. For example, if (cudaMalloc(.) 1⁄41⁄4 cudaSuccess) { if (cudaEventCreate(&event) 1⁄41⁄4 cudaSucess) { . } } else { . } To avoid ...
... call must be checked and some handler written. This makes for very tiresome and highly indented programming. For example, if (cudaMalloc(.) 1⁄41⁄4 cudaSuccess) { if (cudaEventCreate(&event) 1⁄41⁄4 cudaSucess) { . } } else { . } To avoid ...
Сторінка 68
... call encountered some error. If there was an error detected, it prints to ... CUDA calls except for the invocation of kernels. Kernels are the programs ... CUDA SDK, including the GPU computing SDK samples and a debugging environment. You ...
... call encountered some error. If there was an error detected, it prints to ... CUDA calls except for the invocation of kernels. Kernels are the programs ... CUDA SDK, including the GPU computing SDK samples and a debugging environment. You ...
Сторінка 78
... call is the parameters passed. Parameters can be passed via registers or constant memory. The compiler will select ... GPU. This may sound like a huge number to many programmers from the CPU domain, but on a GPU you usually need ...
... call is the parameters passed. Parameters can be passed via registers or constant memory. The compiler will select ... GPU. This may sound like a huge number to many programmers from the CPU domain, but on a GPU you usually need ...
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