Running OpenACC Programs on NVIDIA & AMD GPUs

MessageThis Webinar is over
Date Dec 12, 2013
Time 02:00 PM ET
Cost Free
Mainstream supercomputers and clusters commonly use a 64-bit x86 host processor. They now often include one or more accelerators per node. The most common accelerators today are GPUs. These compute accelerators exploit a high degree of parallelism in an application or algorithm to maximize performance and power efficiency. There are several challenges to effective and productive use of accelerators. These include managing data placement and movement, and expressing parallelism in a form that can be mapped efficiently onto the target hardware. Another challenge, and our focus in this presentation, is designing algorithms and data structures that are of benefit not only for the accelerators of today, but on future accelerator-based systems as well, without significant re-design or re-tuning.
We describe the latest features of current and near-future accelerators. We summarize the current state of programming such systems, including CUDA, OpenCL, OpenACC, and OpenMP. The bulk of the presentation will focus on OpenACC using the PGI Accelerator compilers. We will explore some example applications using currently available GPU accelerators from NVIDIA and AMD. We will explore how to determine when a region of code is suitable for an accelerator; managing data allocation and traffic between host and accelerator memories; appropriate data structures for use on accelerators; building programs for NVIDIA and AMD GPUs; and finally, building a single program that will run on either GPU, or on the host itself.
Speaker - Michael Wolfe
Michael Wolfe is a compiler engineer at The Portland Group where he is involved in deep compiler analysis and optimizations. He has published one textbook, "High Performance Compilers for Parallel Computing", a monograph, "Optimizing Supercompilers for Supercomputers", and many technical papers and articles.

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