Runtime Code Generation for Interpreted Domain-Specific Modeling Languages.

Domain-specific modeling languages (DSMLs) facilitate concise and succinct model descriptions. DSMLs are commonly realized by defining a custom grammar and executing models in an interpreter. This provides flexibility in language design as well as in the implementation of simulation algorithms. However, this type of implementation can lead to a negative impact on simulation performance in comparison to implementing models in general-purpose programming languages (GPL). To mitigate this problem, we propose using runtime code generation. This allows us to replace specific parts of a model at runtime by compiling generated GPL source code. In this paper, we demonstrate the potential benefit of this concept based on ML-Rules, a DSML for modeling and simulating biochemical reaction networks. Using code generation for arithmetic expressions in ML-Rules' reaction rate functions, we were able to decrease the runtime by up to 40% in complex application scenarios.