CUDA Programming: A Developer's Guide to Parallel Computing with GPUs
Newnes, 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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11 The Death of the Single-Core Solution ................................................................... 12 NVIDIA and CUDA. ... 15 Alternatives to CUDA .
In 2011, NVIDIA CUDA-powered GPUs went on to claim the title of the fastest supercomputer in the world. ... It uses almost 300,000 CPU cores and up to 18,000 GPU boards to achieve between 10 and 20 petaflops of performance.
However, the I7 has at least four processors or cores in one device, or double that if you count its hyperthreading ability as a real processor. A DDR-3 triple-channel memory setup on a I7 Nehalem system would produce the theoretical ...
... the processor first queries the cache, and if the data or instructions are present there, the high-speed cache provides them to the processor. DRAM L3 Cache L2 Cache L1 Instruction L1 Data Processor Core L2 Cache L1 Instruction L1 ...
Sometimes these faulty devices are sold cheaply as either triple- or dual-core devices, with the faulty cores disabled. However, the effect of larger, progressively more inefficient caches ultimately results in higher costs to the end ...
Відгуки відвідувачів - Написати рецензію
Chapter 8 MultiCPU and MultiGPU Solutions
Chapter 9 Optimizing Your Application
Chapter 10 Libraries and SDK
Chapter 11 Designing GPUBased Systems
Chapter 12 Common Problems Causes and Solutions