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

By Shane Cook

If you would like to profit CUDA yet don't have adventure with parallel computing, CUDA Programming: A Developer's advent offers a close consultant to CUDA with a grounding in parallel basics. It starts off by way of introducing CUDA and bringing you on top of things on GPU parallelism and undefined, then delving into CUDA set up. Chapters on middle options together with threads, blocks, grids, and reminiscence specialize in either parallel and CUDA-specific concerns. Later, the e-book demonstrates CUDA in perform for optimizing purposes, adjusting to new undefined, and fixing universal problems.

  • Comprehensive creation to parallel programming with CUDA, for readers new to both
  • Detailed directions support readers optimize the CUDA software program improvement kit
  • Practical ideas illustrate operating with reminiscence, threads, algorithms, assets, and more
  • Covers CUDA on a number of systems: Mac, Linux and home windows with a number of NVIDIA chipsets
  • Each bankruptcy contains workouts to check reader knowledge
  • Show description

    Read or Download CUDA Programming: A Developer's Guide to Parallel Computing with GPUs (Applications of GPU Computing Series) PDF

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    Additional resources for CUDA Programming: A Developer's Guide to Parallel Computing with GPUs (Applications of GPU Computing Series)

    Example text

    Search the Internet and see what is already there before you spend weeks developing a library that, unless you are a CUDA expert, is unlikely to be faster than one that is already available. CONCLUSION So maybe you’re thinking, why develop in CUDA? The answer is that CUDA is currently the easiest language to develop in, in terms of support, debugging tools, and drivers. CUDA has a head start on everything else and has a huge lead in terms of maturity. If your application needs to support hardware other than NVIDIA’s, then the best route currently is to develop under CUDA and then port the application to one of the other APIs.

    This extra level of control comes with a much higher level of required programming knowledge, a higher risk of errors, Conclusion 19 and the consequential time impact that may have on a development schedule. Currently, OpenACC requires directives to specify what areas of code should be run on the GPU, but also in which type of memory data should exist. NVIDIA claims you can get in the order of 5Â-plus speedup using such directives. It’s a good solution for those programmers who need to get something working quickly.

    According to Microsoft, this is something you can typically do in an afternoon’s work if you are familiar with both systems. However, being Windows centric, we’ll exclude DirectCompute from many high-end systems where the various flavors of UNIX dominate. Microsoft are also set to launch Cþþ AMP, an additional set of standard template libraries (STLs), which may appeal more to programmers already familiar with Cþþ-style STLs. CPU alternatives The main parallel processing languages extensions are MPI, OpenMP, and pthreads if you are developing for Linux.

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