17299

Panda: A Compiler Framework for Concurrent CPU-GPU Execution of 3D Stencil Computations on GPU-accelerated Supercomputers

Mohammed Sourouri, Scott B. Baden, Xing Cai
Department of Informatics, University of Oslo, Norway
International Journal of Parallel Programming, 45(3), 2017

@article{sourouri2017panda,

   title={Panda: A Compiler Framework for Concurrent CPU+GPU Execution of 3D Stencil Computations on GPU-accelerated Supercomputers},

   author={Sourouri, Mohammed and Baden, Scott B and Cai, Xing},

   journal={International Journal of Parallel Programming},

   volume={45},

   number={3},

   pages={711–729},

   year={2017},

   publisher={Springer}

}

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This paper describes a new compiler framework for heterogeneous 3D stencil computation on GPU clusters. Our framework consists of a simple directive-based programming model and a tightly integrated source-to-source compiler. Annotated with a small number of directives, sequential stencil codes originally written in C can be automatically parallelized for large-scale GPU clusters. The most distinctive feature of the compiler is its capability to generate state-of-the-art hybrid MPI+CUDA+OpenMP code that uses concurrent CPU+GPU computing to unleash the full potential of powerful GPU clusters. At the same time, the auto-generated hybrid codes hide the overhead of various data motion by overlapping them with computation. Test results on the Titan supercomputer and the Wilkes cluster show that auto-translated codes from our compiler can achieve about 90% of the performance of highly optimized handwritten codes, for both a simple stencil benchmark and a real-world application in cardiac modeling. We thus believe that the user-friendliness and performance delivered by our domain-specific compiler framework allow computational scientists to harness the full power of GPU-accelerated supercomputing without painstaking coding effort.
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