Beyond homogeneous decomposition: scaling long-range forces on Massively Parallel Systems

Abstract

With supercomputers anticipated to expand from thousands to millions of cores, one of the challenges facing scientists is how to effectively utilize this ever-increasing number. We report here an approach that creates a heterogeneous decomposition by partitioning effort according to the scaling properties of the component algorithms. We demonstrate our strategy by developing a capability to model hot dense plasma. We have performed benchmark calculations ranging from millions to billions of charged particles, including a 2.8 billion particle simulation that achieved 259.9 TFlop/s (26% of peak performance) on the 294,912 cpu JUGENE computer at the Jülich Supercomputing Centre in Germany. With this unprecedented simulation capability we have begun an investigation of plasma fusion physics under conditions where both theory and experiment are lacking--in the strongly-coupled regime as the plasma begins to burn. Our strategy is applicable to other problems involving long-range forces (i.e., biological or astrophysical simulations). We believe that the flexible heterogeneous decomposition approach demonstrated here will allow many problems to scale across current and next-generation machines.

DOI: 10.1145/1654059.1654121

Extracted Key Phrases

17 Figures and Tables

Cite this paper

@article{Richards2009BeyondHD, title={Beyond homogeneous decomposition: scaling long-range forces on Massively Parallel Systems}, author={David F. Richards and James N. Glosli and Bor Chan and Milo R. Dorr and Erik W. Draeger and Jean-Luc Fattebert and William D. Krauss and Thomas E. Spelce and Frederick H. Streitz and M. P. Surh and John A. Gunnels}, journal={Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis}, year={2009}, pages={1-12} }