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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Computing speed today is measured in TFLOPS (tera floating-point operations per second), a million times larger than the old MFLOPS measurement (1012 vs. 106). ... In a vector machine the same operation acts on many operands.
The SPEs could also be ordered to perform a stream operation, involving multiple steps, as each SPE is connected to a high-speed ring (Figure 1.5). The problem with this sort of streaming or pipelining approach is it runs only as fast ...
However, these shaders were operations that by their very nature took a set of 3D points that represented a polygon map. The shaders applied the same operation to many such datasets, in a hugely parallel manner, giving huge throughput ...
In the background the OS is having to context switch (swap in and out a set of registers) every time it needs to switch between tasks. As context switching is an expensive operation, typically thousands of cycles, CPU applications tend ...
... wait if necessary, and, only when they have the token, perform the operation. This relies on the programmer to identify shared resources and specifically put in place mechanisms to coordinate updates by multiple threads.
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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