A metascalable computing framework for large spatiotemporal-scale atomistic simulations

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 on emerging 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 multiple parallelization 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 effic...

• Type: Conference
• Year: 2009
• Where: IPPS
• Authors: Ken-ichi Nomura, Richard Seymour, Weiqiang Wang, Hikmet Dursun, Rajiv K. Kalia, Aiichiro Nakano, Priya Vashishta, Fuyuki Shimojo, Lin H. Yang
• Keywords: Data Locality, Distributed And Parallel Computing, IPPS 2009, Parallel Computing Framework, Spatiotemporal Data Locality