Generating Fast Specialized Simulators for Stochastic Reaction Networks via Partial Evaluation

Abstract

Domain-specific modeling languages allow a clear separation between simulation model and simulator and, thus, facilitate the development of simulation models and add to the credibility of simulation results. Partial evaluation provides an effective means for efficiently executing models defined in such languages. However, it also implies some challenges of its own. We illustrate this and solutions 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 different 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. For the purpose of better understanding, we additionally generate intermediate variants, where only some parts are partially evaluated. Akin to profile-guided optimization, we 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 large 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 tools with a speedup of more than an order of magnitude. Technical limitations in regard to the size of the generated code are discussed and overcome using a combination of link-time optimization and code separation. A detailed performance study is undertaken, investigating how and where partial evaluation has the largest effect.

Expressive modeling and fast simulation for dynamic compartments

“…Adopting the approach developed in [63] for dynamic compartments appears impractical. Its performance gain relied to a large degree on optimizing the updates of the dependency network.…”

“…In [7], we developed specialized simulators for specific sub-classes of ML-Rules models, e.g., those that do not exploit compartmental dynamics. In [63], we developed an approach generating an entire simulator optimized for a specific model defined in a rule-based language, such as BioNetGen [36]. After parsing the model, custom C or Rust code was generated.…”

A Fast Embedded Language for Continuous-Time Agent-Based Simulation

“…Here the model code is read, and the source code for an existing general-purpose programming language is automatically generated. Meyer et al ( 2018) and Köster et al (2022) show efficiency improvements through both run-time and ahead-of-time code generation. Further details of this trade-off have been discussed in Warnke (2021), Barringer & Havelund (2011), and Artho et al (2015).…”