Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.18637/jss.v100.i07 | JOURNAL OF STATISTICAL SOFTWARE |
Keywords | DocType | Volume |
ABC, HPC, Spark, MPI, parallel, imbalance, Python library | Journal | 100 |
Issue | ISSN | Citations |
7 | 1548-7660 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ritabrata Dutta | 1 | 6 | 2.52 |
| Schoengens Marcel | 2 | 0 | 0.34 |
| Pacchiardi Lorenzo | 3 | 0 | 0.34 |
| Ummadisingu Avinash | 4 | 0 | 1.35 |
| Widmer Nicole | 5 | 0 | 0.34 |
| Jukka-pekka Onnela | 6 | 475 | 36.55 |
| Antonietta Mira | 7 | 100 | 14.65 |