Abstract
For large scale inverse problems, inference can be tackled with distributed algorithms, dividing the task over multiple computing nodes or cores referred to as workers. Since random sampling methods yield not only estimates but also credibility intervals, we leverage data augmentations and MCMC algorithms to design a distributed sampler. In contrast with usual approaches relying on a client-server architecture, we propose a flexible distributed sampler relying on a Single Program Multiple Data implementation, in which all workers have a similar task. This distributed strategy allows the computing time and volume of communications to be reduced by separately handling blocks of data and parameters on different workers. Experiments on a large synthetic image inpainting problem illustrate the performance of the proposed approach to produce high quality estimates in a small amount of time.
Original language | English |
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Title of host publication | 30th European Signal Processing Conference 2022 |
Publisher | IEEE |
Pages | 2016-2020 |
Number of pages | 5 |
ISBN (Electronic) | 9789082797091 |
DOIs | |
Publication status | Published - 18 Oct 2022 |
Event | 30th European Signal Processing Conference 2022 - Belgrade, Serbia Duration: 29 Aug 2022 → 2 Sept 2022 Conference number: 30 https://2022.eusipco.org/ |
Conference
Conference | 30th European Signal Processing Conference 2022 |
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Abbreviated title | EUSIPCO 2022 |
Country/Territory | Serbia |
City | Belgrade |
Period | 29/08/22 → 2/09/22 |
Internet address |
Keywords
- Markov chain Monte-Carlo methods
- Single Program Multiple Data architecture
- distributed algorithm
- inverse problems
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering