Tamrakar, S., Richmond, P. and D'Souza, R.M. (2016) PI-FLAME: A parallel immune system simulator using the FLAME graphic processing unit environment. Simulation, 93 (1). pp. 69-84. ISSN 0037-5497
Abstract
Agent-based models (ABMs) are increasingly being used to study population dynamics in complex systems, such as the human immune system. Previously, Folcik et al. (The basic immune simulator: an agent-based model to study the interactions between innate and adaptive immunity. Theor Biol Med Model 2007; 4: 39) developed a Basic Immune Simulator (BIS) and implemented it using the Recursive Porous Agent Simulation Toolkit (RePast) ABM simulation framework. However, frameworks such as RePast are designed to execute serially on central processing units and therefore cannot efficiently handle large model sizes. In this paper, we report on our implementation of the BIS using FLAME GPU, a parallel computing ABM simulator designed to execute on graphics processing units. To benchmark our implementation, we simulate the response of the immune system to a viral infection of generic tissue cells. We compared our results with those obtained from the original RePast implementation for statistical accuracy. We observe that our implementation has a 13× performance advantage over the original RePast implementation.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 Sage. This is an author produced version of a paper subsequently published in SIMULATION. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Agent-based models; innate immune system; adaptive immune system; FLAME GPU |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC) EP/N018869/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 09 Mar 2017 14:30 |
Last Modified: | 01 Nov 2017 13:21 |
Published Version: | https://doi.org/10.1177/0037549716673724 |
Status: | Published |
Publisher: | SAGE Publications |
Refereed: | Yes |
Identification Number: | 10.1177/0037549716673724 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:113149 |