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Evaluating Trade-offs in Potential Exascale Interconnect Technologies

Hemmert, Karl S.; Bair, Ray; Bhatale, Abhinav; Groves, Taylor; Jain, Nikhil; Lewis, Cannada; Mubarak, Misbah; Pakin, Scott D.; Ross, Robert; Wilke, Jeremiah

This report details work to study trade-offs in topology and network bandwidth for potential interconnects in the exascale (2021-2022) timeframe. The work was done using multiple interconnect models across two parallel discrete event simulators. Results from each independent simulator are shown and discussed and the areas of agreement and disagreement are explored.

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TYPE Other Report YEAR 2020
DOIOSTI

MFNets: Multifidelity data-driven networks for Bayesian learning and prediction

International Journal for Uncertainty Quantification

Gorodetsky, Alex; Jakeman, John D.; Geraci, Gianluca; Eldred, Michael

This paper presents a multifidelity uncertainty quantification framework called MFNets. We seek to address three existing challenges that arise when experimental and simulation data from different sources are used to enhance statistical estimation and prediction with quantified uncertainty. Specifically, we demonstrate that MFNets can (1) fuse heterogeneous data sources arising from simulations with different parameterizations, e.g simulation models with different uncertain parameters or data sets collected under different environmental conditions; (2) encode known relationships among data sources to reduce data requirements; and (3) improve the robustness of existing multi-fidelity approaches to corrupted data. MFNets construct a network of latent variables (LVs) to facilitate the fusion of data from an ensemble of sources of varying credibility and cost. These LVs are posited as explanatory variables that provide the source of correlation in the observed data. Furthermore, MFNets provide a way to encode prior physical knowledge to enable efficient estimation of statistics and/or construction of surrogates via conditional independence relations on the LVs. We highlight the utility of our framework with a number of theoretical results which assess the quality of the posterior mean as a frequentist estimator and compare it to standard sampling approaches that use single fidelity, multilevel, and control variate Monte Carlo estimators. We also use the proposed framework to derive the Monte Carlo-based control variate estimator entirely from the use of Bayes rule and linear-Gaussian models -- to our knowledge the first such derivation. Finally, we demonstrate the ability to work with different uncertain parameters across different models.

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TYPE Journal Article YEAR 2020
DOIOSTI
Results 1026–1050 of 9,998
Results 1026–1050 of 9,998