Specialising simulator generators for high-performance Monte-Carlo methods

Keller, G., Chaffey-Millar, H., Chakravarty, M. M. T., Stewart, D., & Barner-Kowollik, C. (2007) Specialising simulator generators for high-performance Monte-Carlo methods. In UNSPECIFIED.

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We address the tension between software generality and performance in the domain of simulations based on Monte-Carlo methods. We simultaneously achieve generality and high performance by a novel development methodology and software architecture centred around the concept of a specialising simulator generator. Our approach combines and extends methods from functional programming, generative programming, partial evaluation, and runtime code generation. We also show how to generate parallelised simulators. We evaluated our approach by implementing a simulator for advanced forms of polymerisation kinetics. We achieved unprecedented performance, making Monte-Carlo methods practically useful in an area that was previously dominated by deterministic PDE solvers. This is of high practical relevance, as Monte-Carlo simulations can provide detailed microscopic information that cannot be obtained with deterministic solvers. © Springer-Verlag Berlin Heidelberg 2008.

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ID Code: 99152
Item Type: Conference Item (UNSPECIFIED)
Refereed: Yes
Additional Information: Conference code: 71287
Export Date: 5 September 2016
Correspondence Address: Keller, G.; Programming Languages and Systems, School of Computer Science and Engineering, University of New South WalesAustralia; email: keller@cse.unsw.edu.au
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Keywords: Microscopic information, Simulator generators, Codes (symbols), Functional programming, Monte Carlo methods, Partial differential equations, Software architecture
ISBN: 9783540774419
ISSN: 03029743
Divisions: Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Current > Institutes > Institute for Future Environments
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 22 Sep 2016 04:50
Last Modified: 26 Sep 2016 05:02

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