2021/07/30 by Linus Seelinger, Anne Reinarz, Seelinger, Linus +9 · 1 citation
Computer Science · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Markov Chains and Monte Carlo Methods #Mathematical Software (cs.MS) #Numerical Analysis (math.NA) #Target Tracking and Data Fusion in Sensor Networks
paper · pdf · doi:10.48550/arxiv.2107.14552
openalex publication_date 2021/07/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Numerical models of complex real-world phenomena often necessitate High\nPerformance Computing (HPC). Uncertainties increase problem dimensionality\nfurther and pose even greater challenges.\n We present a parallelization strategy for multilevel Markov chain Monte\nCarlo, a state-of-the-art, algorithmically scalable Uncertainty Quantification\n(UQ) algorithm for Bayesian inverse problems, and a new software framework\nallowing for large-scale parallelism across forward model evaluations and the\nUQ algorithms themselves. The main scalability challenge presents itself in the\nform of strong data dependencies introduced by the MLMCMC method, prohibiting\ntrivial parallelization.\n Our software is released as part of the modular and open-source MIT UQ\nLibrary (MUQ), and can easily be coupled with arbitrary user codes. We\ndemonstrate it using the DUNE and the ExaHyPE Engine. The latter provides a\nrealistic, large-scale tsunami model in which identify the source of a tsunami\nfrom buoy-elevation data.\n