CUDA Programming: A Developer's Guide to Parallel Computing by Shane Cook

By Shane Cook

If you want to benefit CUDA yet don't have adventure with parallel computing, CUDA Programming: A Developer's creation offers an in depth advisor to CUDA with a grounding in parallel basics. It starts off by way of introducing CUDA and bringing you in control on GPU parallelism and undefined, then delving into CUDA deploy. Chapters on middle ideas together with threads, blocks, grids, and reminiscence specialise in either parallel and CUDA-specific concerns. Later, the e-book demonstrates CUDA in perform for optimizing functions, adjusting to new undefined, and fixing universal problems.

  • Comprehensive advent to parallel programming with CUDA, for readers new to both
  • Detailed directions aid readers optimize the CUDA software program improvement kit
  • Practical recommendations illustrate operating with reminiscence, threads, algorithms, assets, and more
  • Covers CUDA on a number of systems: Mac, Linux and home windows with numerous NVIDIA chipsets
  • Each bankruptcy comprises routines to check reader knowledge
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    Extra resources for CUDA Programming: A Developer's Guide to Parallel Computing with GPUs (Applications of GPU Computing Series)

    Example text

    CUDA, the GPU programming language we’ll explore in this text, can be used in conjunction with both OpenMP and MPI. There is also an OpenMP-like directive version of CUDA (OpenACC) that may be somewhat easier for those familiar with OpenMP to pick up. OpenMP, MPI, and CUDA are increasingly taught at undergraduate and graduate levels in many university computer courses. However, the first experience most serial programmers had with parallel programming was the introduction of multicore CPUs. These, like the parallel environments before them, were largely ignored by all but a few enthusiasts.

    This works well with both an explicit local memory model such as the GPU’s shared memory as well as a CPU-based cache. In the shared memory case you tell the memory management unit to request this data and then go off and perform useful work on another piece of data. In the cache case you can use special cache instructions that allow prefilling of the cache with data you expect the program to use later. The downside of the cache approach over the shared memory approach is eviction and dirty data.

    Thus, the stray pointer issue should result in an 24 CHAPTER 2 Understanding Parallelism with GPUs exception for out-of-bounds memory access, or at the very least localize the bug to the particular process. Data consequently has to be transferred by formally passing messages to or from processes. In many respects the threading model sits well with OpenMP, while the process model sits well with MPI. In terms of GPUs, they map to a hybrid of both approaches. CUDA uses a grid of blocks. This can be thought of as a queue (or a grid) of processes (blocks) with no interprocess communication.

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