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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Neither the tradesperson nor the client knows how long (the latency) this operation will actually take. There may be congestion on the freeway and/or queues at the hardware store (other processes competing for main memory access).
This means instead of one trip to global memory of an unknown latency, we now have twodone to write the old data and one to get the new data. The big advantage of the programmer-controlled on-chip memory is that the programmeris in ...
If using MPI (Message Passing Interface), commonly used in clusters, the latency for this arrangement can be considerable if the Ethernet connections are attached to the Southbridge instead of the PCI-E bus.
... CPU with the buffer being processed on the GPU. GPU-to-CPU and CPU-to-GPU transfers are made over the relatively slow (5 GB/s) PCI-E bus and this dual-buffering method largely hides this latency and keeps both the CPU and GPU busy.
This reduces the overall latency (time to respond to the request), as there is an overhead associated in the hardware with managing each request. As a result of the coalescing, the memory fetch returns with the data for a whole group of ...
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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