Assess įor an existing project, the first step is to assess the application to locate the parts of the code that are responsible for the bulk of the execution time. This guide introduces the Assess, Parallelize, Optimize, Deploy(APOD) design cycle for applications with the goal of helping application developers to rapidly identify the portions of their code that would most readily benefit from GPU acceleration, rapidly realize that benefit, and begin leveraging the resulting speedups in production as early as possible.ĪPOD is a cyclical process: initial speedups can be achieved, tested, and deployed with only minimal initial investment of time, at which point the cycle can begin again by identifying further optimization opportunities, seeing additional speedups, and then deploying the even faster versions of the application into production. In particular, the optimization section of this guide assumes that you have already successfully downloaded and installed the CUDA Toolkit (if not, please refer to the relevant CUDA Installation Guide for your platform) and that you have a basic familiarity with the CUDA C++ programming language and environment (if not, please refer to the CUDA C++ Programming Guide). The following documents are especially important resources:

This guide refers to and relies on several other documents that you should have at your disposal for reference, all of which are available at no cost from the CUDA website. The discussions in this guide all use the C++ programming language, so you should be comfortable reading C++ code. This approach will greatly improve your understanding of effective programming practices and enable you to better use the guide for reference later. As a result, it is recommended that first-time readers proceed through the guide sequentially. While the contents can be used as a reference manual, you should be aware that some topics are revisited in different contexts as various programming and configuration topics are explored.

It presents established parallelization and optimization techniques and explains coding metaphors and idioms that can greatly simplify programming for CUDA-capable GPU architectures. This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA ® CUDA ® GPUs. The programming guide to using the CUDA Toolkit to obtain the best performance from NVIDIA GPUs.

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