ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation
Dublin Core
Title
ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation
Subject
ABC, HPC, Spark, MPI, parallel, imbalance, Python library.
Description
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
(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
Creator
Ritabrata Dutta
Source
https://www.jstatsoft.org/article/view/v100i07
Publisher
University of Warwick
Date
November 2021
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Ritabrata Dutta, “ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation,” Repository Horizon University Indonesia, accessed April 19, 2025, https://repository.horizon.ac.id/items/show/8220.