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Reilly AC, Staid A, Gao M, et al. Tutorial: Parallel Computing of Simulation Models for Risk Analysis. Risk Anal. 2016;36(10):1844-1854doi: 10.1111/risa.12565.
Reilly, A. C., Staid, A., Gao, M., & Guikema, S. D. (2016). Tutorial: Parallel Computing of Simulation Models for Risk Analysis. Risk analysis : an official publication of the Society for Risk Analysis, 36(10), 1844-1854. https://doi.org/10.1111/risa.12565
Reilly, Allison C, et al. "Tutorial: Parallel Computing of Simulation Models for Risk Analysis." Risk analysis : an official publication of the Society for Risk Analysis vol. 36,10 (2016): 1844-1854. doi: https://doi.org/10.1111/risa.12565
Reilly AC, Staid A, Gao M, Guikema SD. Tutorial: Parallel Computing of Simulation Models for Risk Analysis. Risk Anal. 2016 Oct;36(10):1844-1854. doi: 10.1111/risa.12565. Epub 2016 Feb 05. PMID: 26849834.
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Risk Anal. 2016 Oct;36(10):1844-1854. doi: 10.1111/risa.12565. Epub 2016 Feb 05.

Tutorial: Parallel Computing of Simulation Models for Risk Analysis.

Risk analysis : an official publication of the Society for Risk Analysis

Allison C Reilly, Andrea Staid, Michael Gao, Seth D Guikema

Affiliations

  1. Industrial and Operations Research, University of Michigan, Ann Arbor, MI, USA.
  2. Sandia National Laboratories, Discrete Math and Optimization, Albuquerque, NM, USA.
  3. SolarCity, San Mateo, CA, USA.

PMID: 26849834 DOI: 10.1111/risa.12565

Abstract

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.

© 2016 Society for Risk Analysis.

Keywords: Parallel computing; risk analysis

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