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Tutorial: Parallel Computing of Simulation Models for Risk Analysis

Author

Listed:
  • Allison C. Reilly
  • Andrea Staid
  • Michael Gao
  • Seth D. Guikema

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.

Suggested Citation

  • Allison C. Reilly & Andrea Staid & Michael Gao & Seth D. Guikema, 2016. "Tutorial: Parallel Computing of Simulation Models for Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 36(10), pages 1844-1854, October.
    • Handle: RePEc:wly:riskan:v:36:y:2016:i:10:p:1844-1854
    DOI: 10.1111/risa.12565
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    References listed on IDEAS

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    1. Régis Pouillot & Pascal Beaudeau & Jean‐Baptiste Denis & Francis Derouin & AFSSA Cryptosporidium Study Group, 2004. "A Quantitative Risk Assessment of Waterborne Cryptosporidiosis in France Using Second‐Order Monte Carlo Simulation," Risk Analysis, John Wiley & Sons, vol. 24(1), pages 1-17, February.
    2. Vladimir P. Reshetin & James L. Regens, 2003. "Simulation Modeling of Anthrax Spore Dispersion in a Bioterrorism Incident," Risk Analysis, John Wiley & Sons, vol. 23(6), pages 1135-1145, December.
    3. Jason R. W. Merrick & J. Rene Van Dorp & Varun Dinesh, 2005. "Assessing Uncertainty in Simulation‐Based Maritime Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 25(3), pages 731-743, June.
    4. Andrea Staid & Seth Guikema & Roshanak Nateghi & Steven Quiring & Michael Gao, 2014. "Simulation of tropical cyclone impacts to the U.S. power system under climate change scenarios," Climatic Change, Springer, vol. 127(3), pages 535-546, December.
    5. Roy L. Smith, 1994. "Use of Monte Carlo Simulation for Human Exposure Assessment at a Superfund Site," Risk Analysis, John Wiley & Sons, vol. 14(4), pages 433-439, August.
    6. Trond Rafoss, 2003. "Spatial Stochastic Simulation Offers Potential as a Quantitative Method for Pest Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 23(4), pages 651-661, August.
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