Partial evaluation via code generation for static stochastic reaction network models (Software Appendix) : [research data

Abstract

Succinct, declarative, and domain-specific modeling languages have many advantages when creating simulation models. However, it is often challenging to efficiently execute models defined in such languages. We use code generation for model-specific simulators. Code generation has been successfully applied for high-performance algorithms in many application domains. By generating tailored simulators for specific simulation models defined in a domain specific language, we get the best of both worlds: a succinct, declarative and formal presentation of the model and an efficient execution. We illustrate this based on a simple domain-specific language for biochemical reaction networks as well as on the network representation of the established BioNetGen language. We implement two approaches adopting the same simulation algorithms: one generic simulator that parses models at runtime and one generator that produces a simulator specialized to a given model based on partial evaluation and code generation. Akin to profile-guided optimization we also use dynamic execution of the model to further optimize the simulators. The performance of the approaches is carefully benchmarked using representative models of small to mid-sized biochemical reaction networks. The generic simulator achieves a performance similar to state of the art simulators in the domain, whereas the specialized simulator outperforms established simulation algorithms with a speedup of more than an order of magnitude. This repository contains the code generation software as described in the 2020 PADS paper Partial evaluation via code generation for static stochastic reaction network models.