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.
|
З цієї книги
Результати 6-10 із 77
Сторінка 62
... Compiler) for the Mac. XCode can be downloaded from the Apple store. It's not a free product, but is available free of charge to anyone on the Apple Developer program, which includes both development of Macintosh and iPhone/iPad ...
... Compiler) for the Mac. XCode can be downloaded from the Apple store. It's not a free product, but is available free of charge to anyone on the Apple Developer program, which includes both development of Macintosh and iPhone/iPad ...
Сторінка 66
... compiler will be invoked, NVCC or the host compiler. The generated executable file, or fat binary, contains one or more binary executable images for the different GPU generations. It also contains a PTX image, allowing the CUDA runtime ...
... compiler will be invoked, NVCC or the host compiler. The generated executable file, or fat binary, contains one or more binary executable images for the different GPU generations. It also contains a PTX image, allowing the CUDA runtime ...
Сторінка 67
... compiling, at runtime, the PTX code embedded in the executable. Just as with Java, code depositories are supported. Defining ... compiler errors, you learn to interpret over time. Almost all function calls in CUDA return the error type ...
... compiling, at runtime, the PTX code embedded in the executable. Just as with Java, code depositories are supported. Defining ... compiler errors, you learn to interpret over time. Almost all function calls in CUDA return the error type ...
Сторінка 73
... compilers will either automatically translate such blocks or translate them where the programmer marks that this loop can be parallelized. The Intel compiler is particularly good at this. Such compilers can be used to create embedded ...
... compilers will either automatically translate such blocks or translate them where the programmer marks that this loop can be parallelized. The Intel compiler is particularly good at this. Such compilers can be used to create embedded ...
Сторінка 78
... compiler will select the best choice automatically. If using registers, you will use one register for every thread, per parameter passed. Thus, for 128 threads with three parameters, you use 3 Â 128 1⁄4 384 registers. This may sound ...
... compiler will select the best choice automatically. If using registers, you will use one register for every thread, per parameter passed. Thus, for 128 threads with three parameters, you use 3 Â 128 1⁄4 384 registers. This may sound ...
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 |
Інші видання - Показати все
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