Email updates

Keep up to date with the latest news and content from BMC Genomics and BioMed Central.

This article is part of the supplement: Selected articles from the First IEEE International Conference on Computational Advances in Bio and medical Sciences (ICCABS 2011): Genomics

Open Access Research

High-performance biocomputing for simulating the spread of contagion over large contact networks

Keith R Bisset1*, Ashwin M Aji12, Madhav V Marathe12 and Wu-chun Feng123*

Author Affiliations

1 Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, USA

2 Department of Computer Science, Virginia Tech, Blacksburg, Virginia, USA

3 Department of Electrical & Computer Engineering, Virginia Tech, Blacksburg, Virginia, USA

For all author emails, please log on.

BMC Genomics 2012, 13(Suppl 2):S3  doi:10.1186/1471-2164-13-S2-S3

Published: 12 April 2012

Abstract

Background

Many important biological problems can be modeled as contagion diffusion processes over interaction networks. This article shows how the EpiSimdemics interaction-based simulation system can be applied to the general contagion diffusion problem. Two specific problems, computational epidemiology and human immune system modeling, are given as examples. We then show how the graphics processing unit (GPU) within each compute node of a cluster can effectively be used to speed-up the execution of these types of problems.

Results

We show that a single GPU can accelerate the EpiSimdemics computation kernel by a factor of 6 and the entire application by a factor of 3.3, compared to the execution time on a single core. When 8 CPU cores and 2 GPU devices are utilized, the speed-up of the computational kernel increases to 9.5. When combined with effective techniques for inter-node communication, excellent scalability can be achieved without significant loss of accuracy in the results.

Conclusions

We show that interaction-based simulation systems can be used to model disparate and highly relevant problems in biology. We also show that offloading some of the work to GPUs in distributed interaction-based simulations can be an effective way to achieve increased intra-node efficiency.