Titre :

A versatile distributed MCMC algorithm for large scale inverse problems

Auteur(s) :

Thouvenin, Pierre-Antoine [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Centrale Lille
Université de Lille
Repetti, Audrey [Auteur]
Heriot-Watt University [Edinburgh] [HWU]
Chainais, Pierre [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Centrale Lille
Université de Lille

Titre de la manifestation scientifique :

30th European Signal Processing Conference, EUSIPCO 2022

Ville :

Belgrade

Date de début de la manifestation scientifique :

2022-08-29

Discipline(s) HAL :

Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Traitement des images [eess.IV]
Informatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]

Résumé en anglais : [en]

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 ...
Lire la suite >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. Index Terms-Markov chain Monte-Carlo methods, distributed algorithm, inverse problems, Single Program Multiple Data architecture.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL