High-performance biocomputing for simulating the spread of contagion over large contact networks
| dc.contributor.author | Bisset, Keith R. | en |
| dc.contributor.author | Aji, Ashwin M. | en |
| dc.contributor.author | Marathe, Madhav V. | en |
| dc.contributor.author | Feng, Wu-chun | en |
| dc.contributor.department | Electrical and Computer Engineering | en |
| dc.contributor.department | Computer Science | en |
| dc.contributor.department | Fralin Life Sciences Institute | en |
| dc.date.accessioned | 2012-04-12T12:36:16Z | en |
| dc.date.available | 2012-04-12T12:36:16Z | en |
| dc.date.issued | 2012-04-12 | en |
| dc.date.updated | 2012-04-12T12:36:16Z | en |
| dc.description.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. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | BMC Genomics. 2012 Apr 12;13(Suppl 2):S3 | en |
| dc.identifier.doi | https://doi.org/10.1186/1471-2164-13-S2-S3 | en |
| dc.identifier.issn | 1471-2164 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/18651 | en |
| dc.language.iso | en | en |
| dc.publisher | BMC | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.holder | et al.; licensee BioMed Central Ltd. | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | CUDA | en |
| dc.subject | Computational Epidemiology | en |
| dc.subject | Graphics Processing Unit | en |
| dc.subject | Human Immune System Modeling | en |
| dc.title | High-performance biocomputing for simulating the spread of contagion over large contact networks | en |
| dc.title.serial | BMC Genomics | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |