Vector operations for accelerating expensive Bayesian computations - A tutorial guide
|
Published Version
(PDF 605kB)
87043825. Available under License Creative Commons Attribution 4.0. |
Open access copy at publisher website
Description
Many applications in Bayesian statistics are extremely computationally intensive. However, they are often inherently parallel, making them prime targets for modern massively parallel processors. Multi-core and distributed computing is widely applied in the Bayesian community, however, very little attention has been given to fine-grain parallelisation using single instruction multiple data (SIMD) operations that are available on most modern CPUs. In this work, we practically demonstrate, using standard programming libraries, the utility of the SIMD approach for several topical Bayesian applications. Using the C programming language, we show that SIMD can improve the single-core floating point arithmetic performance by up to a factor of 6× compared scalar C code and more than 25× compared with optimised R code. Such improvements are multiplicative to any gains achieved through multi-core processing. We illustrate the potential of SIMD for accelerating Bayesian computations and provide the reader with techniques for exploiting modern massively parallel processing environments.
Impact and interest:
Citation counts are sourced monthly from Scopus and Web of Science® citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads:
Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
ID Code: | 211448 | ||||
---|---|---|---|---|---|
Item Type: | Contribution to Journal (Journal Article) | ||||
Refereed: | Yes | ||||
ORCID iD: |
|
||||
Additional Information: | Funding Statement: C.D. was supported by the Australian Research Council (ARC) under the Discovery Project scheme (DP200102101). S.A.S. was supported by the ARC under the Discovery Project scheme (DP160102544). C.D., S.A.S., and D.J.W. are supported by the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS; CE140100049). C.D. and D.J.W. also acknowledge support from the Centre for Data Science, Queensland University of Technology. Computational resources were provided by the eResearch Office, Queensland University of Technology. | ||||
Measurements or Duration: | 30 pages | ||||
Additional URLs: | |||||
DOI: | 10.1214/21-BA1265 | ||||
ISSN: | 1931-6690 | ||||
Pure ID: | 87043825 | ||||
Divisions: | Current > Research Centres > Centre for Data Science Current > QUT Faculties and Divisions > Faculty of Science Current > Schools > School of Mathematical Sciences |
||||
Funding: | |||||
Copyright Owner: | 2021 International Society for Bayesian Analysis | ||||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||||
Deposited On: | 30 Jun 2021 00:07 | ||||
Last Modified: | 25 Jul 2024 02:39 |
Export: EndNote | Dublin Core | BibTeX
Repository Staff Only: item control page