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Massively Parallel AI on GPGPUs with OpenCL or C++
Today, even mobile devices include powerful GPUs, servers often include clusters of GPGPUs, and the latest generation of consoles contain shiny compute units. What opportunities do these recent changes in hardware offer AI programmers? Which techniques would reduce the amount of power your game uses, speed up offline pre-calculations by an order of magnitude, or significantly increase the amount of data you can process in real-time? This in-depth tutorial will explain (almost) everything you need to know about modern GPUs, how they perform on a variety of AI algorithms, and how best to leverage their power from standard languages such as OpenCL. Using examples in decision making, visibility and path-finding, you'll see where there's low hanging fruit for massive parallelism as well as applications to avoid completely! You'll also discover what's in store for the near future, such as unified architectures, and turning standard C++ programs into hybrid compute kernels.
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