ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation

TL;DR

ABCpy is a modular Python library that simplifies applying Approximate Bayesian Computation in research, enabling easy parallelization and extension for both domain scientists and ABC experts, with a focus on high-performance computing.

Contribution

The paper presents ABCpy, a modular Python library that facilitates large-scale parallel ABC simulations and algorithm development, emphasizing high-performance computing capabilities.

Findings

ABCpy enables easy parallelization of ABC algorithms.

Dynamic scheduling MPI improves performance with imbalanced algorithms.

ABCpy supports development and evaluation of new inference schemes.

Abstract

ABCpy is a highly modular scientific library for Approximate Bayesian Computation (ABC) written in Python. The main contribution of this paper is to document a software engineering effort that enables domain scientists to easily apply ABC to their research without being ABC experts; using ABCpy they can easily run large parallel simulations without much knowledge about parallelization. Further, ABCpy enables ABC experts to easily develop new inference schemes and evaluate them in a standardized environment and to extend the library with new algorithms. These benefits come mainly from the modularity of ABCpy. We give an overview of the design of ABCpy and provide a performance evaluation concentrating on parallelization. This points us towards the inherent imbalance in some of the ABC algorithms. We develop a dynamic scheduling MPI implementation to mitigate this issue and evaluate the various ABC algorithms according to their adaptability towards high-performance computing.