Understanding and improving the trust in results of numerical simulations and scientific data analytics

Franck Cappello, Rinku Gupta, Sheng Di, Emil Constantinescu, Thomas Peterka, Stefan M. Wild

Research output: Chapter in Book/Report/Conference proceedingConference contribution


With ever-increasing execution scale of parallel scientific simulations, potential unnoticed corruptions to scientific data during simulation make users more suspicious about the correctness of floating-point calculations than ever before. In this paper, we analyze the issue of the trust in results of numerical simulations and scientific data analytics. We first classify the corruptions into two categories, nonsystematic corruption and systematic corruption, and also discuss their origins. Then, we provide a formal definition of the trust in simulation and analytical results across multiple areas. We also discuss what kind of result accuracy would be expected from user’s perspective and how to build trust by existing techniques. We finally identify the current gap and discuss two potential research directions based on existing techniques. We believe that this paper will be interesting to the researchers who are working on the detection of potential unnoticed corruptions of scientific simulation and data analytics, in that not only does it provide a clear definition and classification of corruption as well as an in-depth survey on corruption sources, but we also discuss potential research directions/topics based on existing detection techniques.

Original languageEnglish (US)
Title of host publicationEuro-Par 2017
Subtitle of host publicationParallel Processing Workshops - Euro-Par 2017 International Workshops
EditorsDora B. Heras, Luc Bouge
Number of pages12
ISBN (Print)9783319751771
StatePublished - 2018
Externally publishedYes
EventInternational Workshops on Parallel Processing, Euro-Par 2017 - Santiago de Compostela, Spain
Duration: Aug 28 2017Aug 29 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10659 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Workshops on Parallel Processing, Euro-Par 2017
CitySantiago de Compostela


  • Data analytics
  • Numerical simulation
  • Trust

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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