Parallelizing molecular dynamics using spatial decomposition

Abstract

Several algorithms have been used for parallel molecular dynamics, including the replicated algorithm and those based on spatial decompositions. The replicated algorithm stores the entire system's coordinates and forces at each processor, and therefore has a low overhead in maintaining the data distribution. Spatial decompositions distribute the data, providing better locality and scalability with respect to memory and computation. We present EULERGROMOS, a parallelization of the GROMOS molecular dynamics program which is based on a spatial decomposition. EULERGROMOS parallelizes all molecular dynamics phases, with most data structures using O(N/P) memory. The paper focuses on the structure of EULERGROMOS and analyses its performance using molecular systems of current interest in the molecular dynamics community. EULERGROMOS achieves performance increases with as few as twenty atoms per processor. We also compare EULERGROMOS with an earlier parallelization of GROMOS, UHGROMOS, which uses the replicated algorithm.