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High performance uncertainty quantification with parallelized multilevel Markov chain Monte Carlo

Seelinger, Linus and Reinarz, Anne and Rannabauer, Leonhard and Bader, Michael and Bastian, Peter and Scheichl, Robert (2021) 'High performance uncertainty quantification with parallelized multilevel Markov chain Monte Carlo.', SC21: International Conference for High Performance Computing, Networking, Storage and Analysis St. Louis, MO, 14-19 Nov 2021.

Abstract

Numerical models of complex real-world phenomena often necessitate High Performance Computing (HPC). Uncertainties increase problem dimensionality further and pose even greater challenges. We present a parallelization strategy for multilevel Markov chain Monte Carlo, a state-of-the-art, algorithmically scalable Uncertainty Quantification (UQ) algorithm for Bayesian inverse problems, and a new software framework allowing for large-scale parallelism across forward model evaluations and the UQ algorithms themselves. The main scalability challenge presents itself in the form of strong data dependencies introduced by the MLMCMC method, prohibiting trivial parallelization. Our software is released as part of the modular and open-source MIT Uncertainty Quantification Library (MUQ), and can easily be coupled with arbitrary user codes. We demonstrate it using the Distributed and Unified Numerics Environment (DUNE) and the ExaHyPE Engine. The latter provides a realistic, large-scale tsunami model in which we identify the source of a tsunami from buoy-elevation data.

Item Type:Conference item (Paper)
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1145/3458817.3476150
Publisher statement:Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. SC ’21, November 14–19, 2021, St. Louis, MO, USA © 2021 Association for Computing Machinery
Date accepted:No date available
Date deposited:08 December 2021
Date of first online publication:14 November 2021
Date first made open access:08 December 2021

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