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

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...

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Bibliographic Details
Published in:Journal of statistical software Vol. 100; no. 7; pp. 1 - 38
Main Authors: Dutta, Ritabrata, Schoengens, Marcel, Pacchiardi, Lorenzo, Ummadisingu, Avinash, Widmer, Nicole, Künzli, Pierre, Onnela, Jukka-Pekka, Mira, Antonietta
Format: Journal Article
Language:English
Published: Foundation for Open Access Statistics 01.11.2021
Subjects:
abc
hpc
imbalance
mpi
parallel
python library
spark
ISSN:1548-7660, 1548-7660
Online Access:Get full text
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Summary: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.
ISSN:1548-7660
1548-7660
DOI:10.18637/jss.v100.i07