Pretty Fast Analysis: An embarrassingly parallel algorithm for biological simulation analysis

TL;DR

This paper presents a parallel analysis algorithm for biological simulation data that achieves significant speedup on supercomputers, outperforming existing tools in scaling efficiency and analysis speed.

Contribution

The authors developed a highly parallel analysis software that demonstrates superior scaling and performance for biological simulation data analysis compared to existing methods.

Findings

Embarrassingly parallel speedup up to 1024 nodes

Outperforms AMBER's scaling by a factor of 3

Comparable to NAMD's best reported speedup

Abstract

A parallel code has been written in FORTRAN90, C, and MPI for the analysis of biological simulation data. Using a master/slave algorithm, the software operates on AMBER generated trajectory data using either UNIX or MPI file IO, and it supports up to 15 simultaneous function calls. This software has been performance tested on the Ranger Supercomputer on trajectory data of an aqueous bacterial reaction center micelle. Although the parallel reading is poor, the analysis algorithm itself shows embarrassingly parallel speedup up to 1024 compute nodes. At this CPU count the overall scaling of the software compares well NAMD's best reported speedup, and outperforms AMBER's best known scaling by a factor of 3, while using only a small number of function calls and a short trajectory length.