Runtime Code Generation for Interpreted Domain-Specific Modeling Languages

Meyer, Tom; Helms, Tobias; Warnke, Tom; Uhrmacher, Adelinde M.

doi:10.1109/wsc.2018.8632545

Cited by 7 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The complex expressions result in large abstract syntax trees (AST) that need to be parsed during execution. When using an interpreted or reflection-capable language like Java, code generation for these expressions can be introduced relatively simply [66]. For compiled languages such as Rust, a different approach is required.…”

Section: Simulation Enginementioning

confidence: 99%

Expressive modeling and fast simulation for dynamic compartments

Köster,

Henning,

Warnke

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Compartmentalization is vital for cell biological processes. The field of rule-based stochastic simulation has acknowledged this, and many tools and methods have capabilities for compartmentalization. However, mostly, this is limited to a static compartmental hierarchy and does not integrate compartmental changes. Integrating compartmental dynamics is challenging for the design of the modeling language and the simulation engine. The language should support a concise yet flexible modeling of compartmental dynamics. Our work is based on ML-Rules, a rule-based language for multi-level cell biological modeling that supports a wide variety of compartmental dynamics, whose syntax we slightly adapt. To develop an efficient simulation engine for compartmental dynamics, we combine specific data structures and new and existing algorithms and implement them in the Rust programming language. We evaluate the concept and implementation using two case studies from existing cell-biological models. The execution of these models outperforms previous simulations of ML-Rules by two orders of magnitude. Finally, we present a prototype of a WebAssembly-based implementation to allow for a low barrier of entry when exploring the language and associated models without the need for local installation.Author summaryBiochemical dynamics are constrained by and influence the dynamics of cellular compartments. Basic constraints are considered by many modeling and simulation tools, e.g., certain reactions may only occur in specific cellular compartments and at a speed influenced by the compartmental volume. However, to capture the functioning of complex compartmental dynamics such as cell proliferation or the fission or fusion of mitochondria, additional efforts are required from tool designers. These refer to how the modeler can specify these dynamics succinctly and unambiguously and how the resulting model can be executed efficiently. For modeling, we rely on ML-Rules, an expressive, formal rule-based language for modeling biochemical systems, which ships with the required features and which we only slightly adapt in our re-implementation. We design a new simulation engine that combines efficient data structures and various algorithms for efficient simulation. The achieved efficiency will enable thorough analysis, calibration, and validation of compartmental dynamics and, thus, allow the “in-silico” pursuit of research questions for which compartmental dynamics are essential. To further facilitate exploring the interplay of compartmental and non-compartmental dynamics, we exploit recent advances in web technology so that ML-Rules models can be run efficiently in the web browser.

show abstract

Section: Simulation Enginementioning

confidence: 99%

Expressive modeling and fast simulation for dynamic compartments

Köster,

Henning,

Warnke

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The dynamics of these biochemical systems boil down to the binding, creation, or removal of agents. The simple structure of the models means that they can also be transformed into ordinary differential equations (Mendes et al 2009). This is usually a good approximation if populations are large and stochastic effects may be ignored.…”

Section: 4mentioning

confidence: 99%

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

Köster,

Reinhardt,

Hinsch

et al. 2024

JASSS

View full text Add to dashboard Cite

In agent-based simulation methods and applications, discrete timestep approaches prevail. To support continuous-time agent-based simulation, we analyze how methods for simulating population-based Continuous-Time Markov Chains (CMTCs) can be adopted and derive implications for the concrete realization. To corroborate our findings, we develop an efficient internal domain-specific language (DSL) based on ML3, a modeling language for linked lives in demography. The design as an internal DSL, implemented within the Rust programming language, allows the modeler to exploit the complete feature set of the host language, such as data types and structures, when programming decision processes. A concise and expressive modeling of an agent's discrete decisions and behavior introducing exponentially distributed sojourn times can be supported by adapting the concept of guarded commands from population-based CTMCs. The execution of models relies on an optimized version of the direct method. This method is a variant of stochastic simulation algorithms, an established method for executing population-based CTMCs in other application areas, notably biochemistry. To efficiently handle the large set of possible transitions inherent to continuous-time agent-based models, we use a dependency graph whose updating scheme caters to the dynamic dependencies within agent-based models and the need for efficient implementation. The presented case studies include implementations of a continuous-time, agent-based migration model and a comparative performance study based on an extended SIR model of infection spread, allowing us to draw conclusions about the impact of different design choices on efficiency.

show abstract

“…ML3 code is directly interpreted by the simulator while Julia code is compiled to machine binary before execution. It has been shown that even the compilation of only critical pieces of a model at runtime can yield significant efficiency gains (Meyer et al 2018).…”

Section: Runtimementioning

confidence: 99%

Developing Agent-Based Migration Models in Pairs

Reinhardt

Uhrmacher

Hinsch

et al. 2019

2019 Winter Simulation Conference (WSC)

Self Cite

View full text Add to dashboard Cite

Developing a realistic agent-based model of human migration requires particular care. Committing too early to a specific model architecture, design, or language environment can later become costly in terms of the revisions required. To examine specifically the impact of differences in implementation, we have developed two instances of the same model in parallel. One model is realized in the programming language Julia, the underlying execution semantics is of a discrete stepwise stochastic process. The other is realized in an external domain-specific language ML3, based on a continuous-time Markov chain (CTMC) semantics. By developing models in pairs in different approaches, important properties of the target model can be more effectively revealed. In addition, the realization within a programming language and an external domain-specific modeling language respectively, helped identifying crucial features and trade-offs for the future implementation of the model and the design of the domain-specific modeling language.

show abstract

Runtime Code Generation for Interpreted Domain-Specific Modeling Languages

Cited by 7 publications

References 0 publications

Expressive modeling and fast simulation for dynamic compartments

Expressive modeling and fast simulation for dynamic compartments

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

Developing Agent-Based Migration Models in Pairs

Contact Info

Product

Resources

About