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Matem. Mod., 2016, Volume 28, Number 5, Pages 95–108 (Mi mm3732)  

This article is cited in 4 scientific papers (total in 4 papers)

Efficiency of classical molecular dynamics algorithms on supercomputing hardware

Grigory S. Smirnovab, Vladimir V. Stegailovab

a Moscow Institute of Physics and Technology (State University)
b Joint Institute for High Temperatures of Russian Academy of Sciences

Abstract: Development of new HPC architectures proceeds faster than the corresponding adjustment of the algorithms for such fundamental mathematical models as classical molecular dynamics. The wide variety of choices poses the requirement of clear guiding criteria for the computational efficiency of a particular model on a particular hardware. LINPACK benchmark can no longer serve this role. In this work we consider a practical metrics of the time-to-solution versus the computational peak performance. In this metrics we compare different hardware (both legacy and modern) on the example of the LAMMPS software packages widely used for atomistic modeling. We show that the metrics considered can serve as a universal unambiguous scale ranging different combinations of CPUs, accelerators and interconnects.

Keywords: atomistic modeling, CPU architecture, accelerators, peak performance.

Funding Agency Grant Number
Russian Foundation for Basic Research 13-01-12070_офи_м
Russian Academy of Sciences - Federal Agency for Scientific Organizations

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English version:
Mathematical Models and Computer Simulations, 2016, 8:6, 734–743

Document Type: Article
Received: 21.04.2015

Citation: Grigory S. Smirnov, Vladimir V. Stegailov, “Efficiency of classical molecular dynamics algorithms on supercomputing hardware”, Matem. Mod., 28:5 (2016), 95–108; Math. Models Comput. Simul., 8:6 (2016), 734–743

Citation in format AMSBIB
\by Grigory~S.~Smirnov, Vladimir~V.~Stegailov
\paper Efficiency of classical molecular dynamics algorithms on supercomputing hardware
\jour Matem. Mod.
\yr 2016
\vol 28
\issue 5
\pages 95--108
\jour Math. Models Comput. Simul.
\yr 2016
\vol 8
\issue 6
\pages 734--743

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    This publication is cited in the following articles:
    1. Akimov V.S., Silaev D.P., Aksenov A.A., Zhluktov S.V., Savitskiy D.V., Simonov A.S., “Flowvision Scalability on Supercomputers With Angara Interconnect”, Lobachevskii J. Math., 39:9 (2018), 1159–1169  crossref  mathscinet  zmath  isi
    2. Polyakov S.V. Podryga V.O. Puzyrkov D.V., “High Performance Computing in Multiscale Problems of Gas Dynamics”, Lobachevskii J. Math., 39:9 (2018), 1239–1250  crossref  mathscinet  zmath  isi
    3. Semenov A., Mazeev A., Doropheev D., Yusubaliev T., “Unsupervised Graph Anomaly Detection Algorithms Implemented in Apache Spark”, Lobachevskii J. Math., 39:9 (2018), 1262–1269  crossref  mathscinet  zmath  isi
    4. Stegailov V., Agarkov A., Biryukov S., Ismagilov T., Khalilov M., Kondratyuk N., Kushtanov E., Makagon D., Mukosey A., Semenov A., Simonov A., Timofeev A., Vecher V., “Early Performance Evaluation of the Hybrid Cluster With Torus Interconnect Aimed At Molecular-Dynamics Simulations”: Karczewski, K, Parallel Processing and Applied Mathematics (Ppam 2017), Pt i, Lecture Notes in Computer Science, 10777, eds. Wyrzykowski R., Dongarra J., Deelman E., Springer International Publishing Ag, 2018, 327–336  crossref  mathscinet  isi
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