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Loop Aggregation for Approximate Scientific Computing

Abstract : Trading off some accuracy for better performances in scientific computing is an appealing approach to ease the exploration of various alternatives on complex simulation models. Existing approaches involve the application of either time-consuming model reduction techniques or resource-demanding statistical approaches. Such requirements prevent any opportunistic model exploration, e.g., exploring various scenarios on environmental models. This limits the ability to analyse new models for scientists, to support trade-off analysis for decision-makers and to empower the general public towards informed environmental intelligence. In this paper, we present a new approximate computing technique, aka. loop aggregation, which consists in automatically reducing the main loop of a simulation model by aggregating the corresponding spatial or temporal data. We apply this approximate scientific computing approach on a geophysical model of a hydraulic simulation with various input data. The experimentation demonstrates the ability to drastically decrease the simulation time while preserving acceptable results with a minimal set-up. We obtain a median speed-up of 95.13% and up to 99.78% across all the 23 case studies.
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Contributor : June Sallou <>
Submitted on : Friday, April 17, 2020 - 2:39:29 PM
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June Sallou, Alexandre Gauvain, Johann Bourcier, Benoit Combemale, Jean-Raynald de Dreuzy. Loop Aggregation for Approximate Scientific Computing. International Conference on Computational Science, Jun 2020, Amsterdam, Netherlands. pp.141-155, ⟨10.1007/978-3-030-50417-5_11⟩. ⟨hal-02545875⟩



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