4.7 Article

Implementing molecular dynamics on hybrid high performance computers - Particle-particle particle-mesh

Journal

COMPUTER PHYSICS COMMUNICATIONS
Volume 183, Issue 3, Pages 449-459

Publisher

ELSEVIER
DOI: 10.1016/j.cpc.2011.10.012

Keywords

Molecular dynamics; Electrostatics; Particle mesh; GPU; Hybrid parallel computing

Funding

  1. Office of Advanced Scientific Computing Research, Office of Science, U.S. Department of Energy [DE-AC05-00OR22725]
  2. UT-Battelle, LLC
  3. Office of Science of the U.S. Department of Energy [DE-AC05-00OR22725]
  4. U.S. Department of Energy [DE-AC04-94AL85000]
  5. National Science Foundation [CHE-09-46358]
  6. National Institute for Computational Science [UT-NTNL0039]
  7. Direct For Computer & Info Scie & Enginr
  8. Division Of Computer and Network Systems [0958854] Funding Source: National Science Foundation

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The use of accelerators such as graphics processing units (GPUs) has become popular in scientific computing applications due to their low cost, impressive floating-point capabilities, high memory bandwidth, and low electrical power requirements. Hybrid high-performance computers, machines with nodes containing more than one type of floating-point processor (e.g. CPU and GPU), are now becoming more prevalent due to these advantages. In this paper, we present a continuation of previous work implementing algorithms for using accelerators into the LAMMPS molecular dynamics software for distributed memory parallel hybrid machines. In our previous work, we focused on acceleration for short-range models with an approach intended to harness the processing power of both the accelerator and (multi-core) CPUs. To augment the existing implementations, we present an efficient implementation of long-range electrostatic force calculation for molecular dynamics. Specifically, we present an implementation of the particle-particle particle-mesh method based on the work by Harvey and De Fabritiis. We present benchmark results on the Keeneland InfiniBand GPU cluster. We provide a performance comparison of the same kernels compiled with both CUDA and OpenCL. We discuss limitations to parallel efficiency and future directions for improving performance on hybrid or heterogeneous computers. (C) 2011 Elsevier B.V. All rights reserved.

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