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Performance Evaluation of NWChem Ab-Initio Molecular Dynamics (AIMD) Simulations on the Intel® Xeon Phi™ Processor

  • Eric J. BylaskaEmail author
  • Mathias Jacquelin
  • Wibe A. de Jong
  • Jeff R. Hammond
  • Michael Klemm
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10524)

Abstract

Ab-initio Molecular Dynamics (AIMD) methods are an important class of algorithms, as they enable scientists to understand the chemistry and dynamics of molecular and condensed phase systems while retaining a first-principles-based description of their interactions. Many-core architectures such as the Intel® Xeon Phi™ processor are an interesting and promising target for these algorithms, as they can provide the computational power that is needed to solve interesting problems in chemistry. In this paper, we describe the efforts of refactoring the existing AIMD plane-wave method of NWChem from an MPI-only implementation to a scalable, hybrid code that employs MPI and OpenMP to exploit the capabilities of current and future many-core architectures. We describe the optimizations required to get close to optimal performance for the multiplication of the tall-and-skinny matrices that form the core of the computational algorithm. We present strong scaling results on the complete AIMD simulation for a test case that simulates 256 water molecules and that strong-scales well on a cluster of 1024 nodes of Intel Xeon Phi processors. We compare the performance obtained with a cluster of dual-socket Intel® Xeon® E5–2698v3 processors.

Keywords

Xeon Phi Many-core Chemistry AIMD Ab-initio Molecular dynamics 

Notes

Acknowledgment

This work was supported by the NWChem project in the William R. Wiley Environmental Molecular Sciences Laboratory (EMSL), the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research ECP program (NWChemEx project), and E.J.B was also supported by the the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division at PNNL, DE-AC06-76RLO 1830. EMSL operations are supported by the DOE’s Office of Biological and Environmental Research. M.J. and W.A.D. were partially supported by the Scientific Discovery through Advanced Computing (SciDAC) program funded by U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research and Basic Energy Sciences. In particular, M.J. was supported by the FASTMath SciDAC institute. We wish to thank the Scientific Computing Staff, Office of Energy Research, and the U. S. Department of Energy for support through the NERSC NESAP program the National Energy Research Scientific Computing Center (Berkeley, CA). This work was also supported by Intel as part of its Intel Parallel Computing Centers effort. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Eric J. Bylaska
    • 1
    Email author
  • Mathias Jacquelin
    • 2
  • Wibe A. de Jong
    • 2
  • Jeff R. Hammond
    • 3
  • Michael Klemm
    • 4
  1. 1.Environmental Molecular Sciences LaboratoryPacific Northwest National LaboratoryRichlandUSA
  2. 2.Computational Research DivisionLawrence Berkeley National LaboratoryBerkeleyUSA
  3. 3.Data Center Group, Intel CorporationPortlandUSA
  4. 4.Software and Services GroupIntel Deutschland GmbHFeldkirchenGermany

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