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Implementing molecular dynamics on hybrid high performance computers – short range forces

W. Michael Brown, Peng Wang, Steven J. Plimpton, Arnold N. Tharrington
National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Computer Physics Communications (22 December 2010)

@article{brown2010implementing,

   title={Implementing molecular dynamics on hybrid high performance computers-short range forces},

   author={Brown, W.M. and Wang, P. and Plimpton, S.J. and Tharrington, A.N.},

   journal={Computer Physics Communications},

   issn={0010-4655},

   year={2010},

   publisher={Elsevier}

}

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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 more than one type of floating-point processor, are now becoming more prevalent due to these advantages. In this work, we discuss several important issues in porting a large molecular dynamics code for use on parallel hybrid machines – (1) choosing a hybrid parallel decomposition that works on central processing units (CPUs) with distributed memory and accelerator cores with shared memory, (2) minimizing the amount of code that must be ported for efficient acceleration, (3) utilizing the available processing power from both multi-core CPUs and accelerators, and (4) choosing a programming model for acceleration. We present our solution to each of these issues for short-range force calculation in the molecular dynamics package LAMMPS, however, the methods can be applied in many molecular dynamics codes. Specifically, we describe algorithms for efficient short range force calculation on hybrid high-performance machines. We describe an approach for dynamic load balancing of work between CPU and accelerator cores. We describe the Geryon library that allows a single code to compile with both CUDA and OpenCL for use on a variety of accelerators. Finally, we present results on a parallel test cluster containing 32 Fermi GPUs and 180 CPU cores.
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