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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For programmers already familiar with parallel programming concepts and CUDA, we'll be discussing in detail the architecture of the GPUs and how to get the most from each, including the latest Fermi and Kepler hardware.
A single Fermi GPU card today has a theoretical peak in excess of 1 teraflop of performance. The Cray-2 was a significant improvement on the Cray-1. It used a shared memory architecture, split into banks. These were connected to one, ...
At this point we were moving from the G80 hardware to the G200 and then in 2010 to the Fermi evolution. This is driven by the introduction of massively parallel hardware. The G80 is a 128 CUDA core device, the G200 is a 256 CUDA core ...
Also be careful with the GF100 (Fermi) series, as the Tesla variant has double the number of double-precision units found in the standard desktop units, so achieves significantly better double-precision throughput.
However, for most embarrassingly parallel programs it's just a case of thinking of one thread generating a single output data point. A typical GPU has on the order of 24 K active threads. On Fermi GPUs you can define 65,535 Â 65,535 Â ...
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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