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Tutorial: Parallel Computing of Simulation Models for Risk Analysis
Author(s) -
Reilly Allison C.,
Staid Andrea,
Gao Michael,
Guikema Seth D.
Publication year - 2016
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/risa.12565
Subject(s) - computer science , leverage (statistics) , embarrassingly parallel , matlab , software , supercomputer , odds , point (geometry) , code (set theory) , limit (mathematics) , parallel computing , computational science , programming language , massively parallel , machine learning , mathematics , mathematical analysis , logistic regression , geometry , set (abstract data type)
Simulation models are widely used in risk analysis to study the effects of uncertainties on outcomes of interest in complex problems. Often, these models are computationally complex and time consuming to run. This latter point may be at odds with time‐sensitive evaluations or may limit the number of parameters that are considered. In this article, we give an introductory tutorial focused on parallelizing simulation code to better leverage modern computing hardware, enabling risk analysts to better utilize simulation‐based methods for quantifying uncertainty in practice. This article is aimed primarily at risk analysts who use simulation methods but do not yet utilize parallelization to decrease the computational burden of these models. The discussion is focused on conceptual aspects of embarrassingly parallel computer code and software considerations. Two complementary examples are shown using the languages MATLAB and R. A brief discussion of hardware considerations is located in the Appendix.