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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Vydáno v:Journal of statistical software Ročník 100; číslo 7; s. 1 - 38
Hlavní autoři: Dutta, Ritabrata, Schoengens, Marcel, Pacchiardi, Lorenzo, Ummadisingu, Avinash, Widmer, Nicole, Künzli, Pierre, Onnela, Jukka-Pekka, Mira, Antonietta
Médium: Journal Article
Jazyk:angličtina
Vydáno: Foundation for Open Access Statistics 01.11.2021
Témata:
abc
hpc
imbalance
mpi
parallel
python library
spark
ISSN:1548-7660, 1548-7660
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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.
AbstractList 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.
Author Mira, Antonietta
Pacchiardi, Lorenzo
Onnela, Jukka-Pekka
Ummadisingu, Avinash
Widmer, Nicole
Dutta, Ritabrata
Künzli, Pierre
Schoengens, Marcel
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SubjectTerms abc
hpc
imbalance
mpi
parallel
python library
spark
Title ABCpy : A High-Performance Computing Perspective to Approximate Bayesian Computation
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