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A massively parallel adaptive fast-multipole method on heterogeneous architectures

Ilya Lashuk, Aparna Chandramowlishwaran, Harper Langston, Tuan A. Nguyen, Rahul Sampath, Aashay Shringarpure, Richard Vuduc, Lexing Ying, Denis Zorin, George Biros
Georgia Institute of Technology, Atlanta, GA 30332
In SC ’09: Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis (2009), pp. 1-12

@conference{lashuk2009massively,

   title={A massively parallel adaptive fast-multipole method on heterogeneous architectures},

   author={Lashuk, I. and Chandramowlishwaran, A. and Langston, H. and Nguyen, T.A. and Sampath, R. and Shringarpure, A. and Vuduc, R. and Ying, L. and Zorin, D. and Biros, G.},

   booktitle={Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis},

   pages={1–12},

   year={2009},

   organization={ACM}

}

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We present new scalable algorithms and a new implementation of our kernel-independent fast multipole method (Ying et al. ACM/IEEE SC ’03), in which we employ both distributed memory parallelism (via MPI) and shared memory/streaming parallelism (via GPU acceleration) to rapidly evaluate two-body non-oscillatory potentials. On traditional CPU-only systems, our implementation scales well up to 30 billion unknowns on 65K cores (AMD/CRAY-based Kraken system at NSF/NICS) for highly non-uniform point distributions. On GPU-enabled systems, we achieve 30x speedup for problems of up to 256 million points on 256 GPUs (Lincoln at NSF/NCSA) over a comparable CPU-only based implementations.
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