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Blue Matter: Scaling of N-body simulations to one atom per node

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9 Author(s)
Fitch, B.G. ; IBM Research Division, Thomas J. Watson Research Center, P.O. Box 218, Yorktown Heights, New York 10598, USA ; Rayshubskiy, A. ; Eleftheriou, M. ; Ward, T.J.C.
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N-body simulations present some of the most interesting challenges in the area of massively parallel computing, especially when the object is to improve the time to solution for a fixed-size problem. The Blue Matter molecular simulation framework was developed specifically to address these challenges, to explore programming models for massively parallel machine architectures in a concrete context, and to support the scientific goals of the IBM Blue Gene® Project. This paper reviews the key issues involved in achieving ultrastrong scaling of methodologically correct biomolecular simulations, particularly the treatment of the long-range electrostatic forces present in simulations of proteins in water and membranes. Blue Matter computes these forces using the particle-particle particle-mesh Ewald (P3ME) method, which breaks the problem up into two pieces, one that requires the use of three-dimensional fast Fourier transforms with global data dependencies and another that involves computing interactions between pairs of particles within a cutoff distance. We summarize our exploration of the parallel decompositions used to compute these finite-ranged interactions, describe some of the implementation details involved in these decompositions, and present the evolution of strong-scaling performance achieved over the course of this exploration, along with evidence for the quality of simulation achieved.

Note: The Institute of Electrical and Electronics Engineers, Incorporated is distributing this Article with permission of the International Business Machines Corporation (IBM) who is the exclusive owner. The recipient of this Article may not assign, sublicense, lease, rent or otherwise transfer, reproduce, prepare derivative works, publicly display or perform, or distribute the Article.  

Published in:

IBM Journal of Research and Development  (Volume:52 ,  Issue: 1.2 )