Physically-based visual simulation on graphics hardware
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
Hardware-based simulation and collision detection for large particle systems
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
Adaptive algorithms for the dynamic distribution and parallel execution of agent-based models
Journal of Parallel and Distributed Computing - Special issue on parallel bioinspired algorithms
Impostors and pseudo-instancing for GPU crowd rendering
Proceedings of the 4th international conference on Computer graphics and interactive techniques in Australasia and Southeast Asia
Performance Improvement Methodology for ClearSpeed's CSX600
ICPP '07 Proceedings of the 2007 International Conference on Parallel Processing
Proceedings of the ACM/SIGDA international symposium on Field programmable gate arrays
Programming Massively Parallel Processors: A Hands-on Approach
Programming Massively Parallel Processors: A Hands-on Approach
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Agent-based modeling is increasingly being used for computer simulation of complex biological systems. An agent-based model (ABM) is a bottom-up simulation where the bulk dynamics of the model result from the local interactions of its individual constituents or agents. However, due to emergent qualities of ABMs, bulk behaviors may be sensitive to the size of the model as determined by the population of individuals. Therefore, in certain circumstances it may be critical to closely match the simulation size with the actual system. This may be particularly true in biological systems, where multiple large-scale heterogeneous populations can range into millions or even billions of individual cells/agents. Most existing ABM simulation toolkits are designed for serial computing and canno*t effectively simulate such mega-scale systems from a run-time standpoint. In this paper, we investigate data-parallel ABM implementations on graphics processing units to address the scalability issue of ABMs. As an example, we have implemented an abstracted version of the Systemic Inflammatory Response Syndrome ABM. We also implemented a serial version to confirm statistical accuracy. Our results show that parallelization on graphics processing units offers a substantial gain in performance without a loss in accuracy.