Abstract
The use of accelerators such as general-purpose graphics processing units
(GPGPUs) have 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
many-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.
We describe algorithms for efficient short range force calculation
on hybrid high performance machines. We describe a new 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 GPGPUs and 180 CPU cores.
