A Metascalable Computing Framework for Large Spatiotemporal-Scale Atomistic Simulations [electronic resource].
- Washington, D.C. : United States. Dept. of Energy, 2009.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
- Physical Description:
- PDF-file: 16 pages; size: 3.1 Mbytes
- Additional Creators:
- Lawrence Berkeley National Laboratory
United States. Department of Energy
United States. Department of Energy. Office of Scientific and Technical Information
- 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 long-time 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 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/ensemble parallel 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).
- Published through SciTech Connect.
Presented at: The 23rd IEEE International Parallel & Distributed Processing Symposium, Rome, Italy, May 25 - May 29, 2009.
Nomura, K; Wang, W; Yang, L H; Seymour, R; Nakano, A; Vashishta, P; Kalia, R; Shimojo, F.
- Funding Information:
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