Partial evaluation and automatic program generation
Partial evaluation and automatic program generation
Tempo: specializing systems applications and beyond
ACM Computing Surveys (CSUR) - Special issue: electronic supplement to the September 1998 issue
Generative programming: methods, tools, and applications
Generative programming: methods, tools, and applications
Haskell '04 Proceedings of the 2004 ACM SIGPLAN workshop on Haskell
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Coalescing executions for fast uncertainty analysis
Proceedings of the 33rd International Conference on Software Engineering
Combinators for impure yet hygienic code generation
Proceedings of the ACM SIGPLAN 2014 Workshop on Partial Evaluation and Program Manipulation
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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.