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A metascalable computing framework for large spatiotemporal-Scale atomistic simulations

  • Ken Ichi Nomura
  • , Richard Seymour
  • , Weiqiang Wang
  • , Hikmet Dursun
  • , Rajiv K. Kalia
  • , Aiichiro Nakano
  • , Priya Vashishta
  • , Fuyuki Shimojo
  • , Lin H. Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

A metascalable (or "design once, scale on new architectures") parallel computing framework has been developed for large spatiotemporal-scale atomistic simulations of materials based on spatiotemporal data locality principles, which is expected to scale onemerging multipetaflops architectures. The framework consists of: (1) an embedded divide-and-conquer (EDC) algorithmic framework based on spatial locality to design linear-scaling algorithms for high complexity problems; (2) a space-time-ensemble parallel (STEP) approach based on temporal locality to predict longtime dynamics, while introducing multipleparallelization axes; and (3) a tunable hierarchical cellular decomposition (HCD) parallelization framework to map these O(N) algorithms onto a multicore cluster based on hybrid implementation combining message passing and critical section-free multithreading. The EDC-STEP-HCD framework exposes maximal concurrency and data locality, thereby achieving: (1) inter-node parallel efficiency well over 0.95 for 218 billion-atom molecular-dynamics and 1.68 trillion electronic-degrees-of-freedom quantum-mechanical simulations on 212,992 IBM BlueGene/L processors (superscalability); (2) high intra-node, multithreading parallel efficiency (nanoscalability); and (3) nearly perfect time/ensembleparallel efficiency (eon-scalability). The spatiotemporal scale covered by MD simulation on a sustained petaflops computer per day (i.e. petaflops day of computing) is estimated as NT = 2.14 (e.g. N = 2.14 million atoms for T = 1 microseconds). © 2009 IEEE.
Original languageEnglish
Title of host publicationIPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium
DOIs
StatePublished - Nov 25 2009

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