Efficiently linking fast simulation backends with easy python frontends
Manual binding and semi-automated interface generation for efficient simulation backends with user-friendly frontends: A~demonstration for epidemic use cases
Software Engineering
Summary
Doing detailed disease spread simulations is slow if only done with common user-friendly tools like Python or R. Using faster languages like C++ speeds things up but is harder to connect to interactive tools. The authors show how to connect speedy C++ disease models with Python in a simpler way by automatically creating the necessary code, helping scientists quickly build and run better simulations.
What this means in practice
- •For epidemic modeling teams: Run detailed infectious disease simulations in Python with faster C++ model backends for quicker public health decision support.
- •For scientific software developers: Easily create and maintain bindings between C++ mathematical simulation libraries and user-friendly Python interfaces using semi-automated code generation.
Authors
Maximilian Betz, Daniel-Michel Richter, Daniel Abele, René Schmieding, Henrik Zunker, Martin J. Kühn
Abstract
Challenges and chances of mathematical software are directly entangled with the programming language used. Depending on the background of developers and users, conflicts between different goals may arise. While several, e.g., scripting languages allow a low barrier and user-friendly entry to mathematical modeling and simulation, corresponding implementations will most likely not allow timely outcomes for detailed simulations. In infectious disease modeling, while several large-scale software frameworks rely on C or C++ implementations, most models are implemented in Python or R. While the latter allow for fast prototyping and more interactive development, it has been shown for several applications that C or C++ implementations outperform plain Python or R implementations by factors between one and three orders of magnitude. In particular, in times of pandemics, the urgent need for swift estimation of the system's dynamics makes the use of large-scale models in these noncompiled languages prohibitive. In order to create synergies and close the gap between scientists from the application and the computational domain, binding libraries allow to execute entire, compiled software frameworks from languages such as Python. However, with iterative model development, extension, or addition of new models, implementing bindings can represent another relevant barrier to developers of the C or C++ backends. In this paper, we show how a large-scale software framework with a multitude of infectious disease models can be efficiently exposed to Python. In addition, we suggest a semi-automated way to generate interface code by analyzing generic C++ code through abstract syntax trees, enabling a modularized approach working with intermediate representations.