Paper Info
Title
Data-parallel algorithms for agent-based model simulation of tuberculosis on graphics processing units
Abstract
Agent-based modeling has been recognized as a method to bridge the translational gap in integrative systems biology. However, the computational complexity of agent-based models at biologically relevant scales makes simulation impractical on traditional CPU-based serial computing. In this paper we present a series of algorithms for simulating large scale agent-based models on graphics processing units (GPUs). GPUs have recently emerged as a powerful and economical computing platform for certain applications in scientific computing. As a test case, we have implemented an agent-based model of tuberculosis. This model simulates the interaction of the human immune system in the lung with Mycobacterium tuberculosis and tracks the formation of characteristic structures called granulomas. The model uses mobile agents to represent immune cells such as T cells and macrophages, field equations representing effector chemokines, and bacteria. Algorithms were implemented and benchmarked against a CPU implementation. Our benchmarks show performance gains of over 100 for moderately sized models. This opens the possibility of efficiently simulating realistically sized models on desktop computers.
Year
DOI
Venue
2009
10.1145/1639809.1639831
SpringSim
Keywords
Field
DocType
integrable system,mobile agent,scientific computing,computational complexity,human immune system,gpgpu
Graphics,Agent-based model,Simulation,Computer science,Parallel algorithm,Systems biology,Field equation,General-purpose computing on graphics processing units,Computational complexity theory
Conference
Citations 
PageRank 
References 
16
1.36
12
Authors
4
Name
Order
Citations
PageRank
Roshan D'Souza1987.60
Mikola Lysenko2746.13
Simeone Marino3314.35
Denise E. Kirschner4181.76