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Efficient Ranking and Selection in Parallel Computing Environments

Author

Listed:
  • Eric C. Ni

    (School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853)

  • Dragos F. Ciocan

    (Technology and Operations Management, INSEAD, Fontainebleau, France 77300)

  • Shane G. Henderson

    (School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853)

  • Susan R. Hunter

    (School of Industrial Engineering, Purdue University, West Lafayette, Indiana 47907)

Abstract

The goal of ranking and selection (R&S) procedures is to identify the best stochastic system from among a finite set of competing alternatives. Such procedures require constructing estimates of each system’s performance, which can be obtained simultaneously by running multiple independent replications on a parallel computing platform. Nontrivial statistical and implementation issues arise when designing R&S procedures for a parallel computing environment. We propose several design principles for parallel R&S procedures that preserve statistical validity and maximize core utilization, especially when large numbers of alternatives or cores are involved. These principles are followed closely by our parallel Good Selection Procedure (GSP), which, under the assumption of normally distributed output, (i) guarantees to select a system in the indifference zone with high probability, (ii) in tests on up to 1,024 parallel cores runs efficiently, and (iii) in an example uses smaller sample sizes compared to existing parallel procedures, particularly for large problems (over 10 6 alternatives). In our computational study we discuss three methods for implementing GSP on parallel computers, namely the Message-Passing Interface (MPI), Hadoop MapReduce, and Spark, and show that Spark provides a good compromise between the efficiency of MPI and robustness to core failures.

Suggested Citation

  • Eric C. Ni & Dragos F. Ciocan & Shane G. Henderson & Susan R. Hunter, 2017. "Efficient Ranking and Selection in Parallel Computing Environments," Operations Research, INFORMS, vol. 65(3), pages 821-836, June.
    • Handle: RePEc:inm:oropre:v:65:y:2017:i:3:p:821-836
    DOI: 10.1287/opre.2016.1577
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    References listed on IDEAS

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    Cited by:

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    2. Taylor, Simon J.E., 2019. "Distributed simulation: state-of-the-art and potential for operational research," European Journal of Operational Research, Elsevier, vol. 273(1), pages 1-19.
    3. Yishuang Hu & Yi Ding & Zhiguo Zeng, 2022. "Redundancy optimization for multi-state series-parallel systems using ordinal optimization-based-genetic algorithm," Journal of Risk and Reliability, , vol. 236(1), pages 66-78, February.
    4. Saeid Delshad & Amin Khademi, 2020. "Information theory for ranking and selection," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(4), pages 239-253, June.
    5. Zhongshun Shi & Yijie Peng & Leyuan Shi & Chun-Hung Chen & Michael C. Fu, 2022. "Dynamic Sampling Allocation Under Finite Simulation Budget for Feasibility Determination," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 557-568, January.
    6. Haihui Shen & L. Jeff Hong & Xiaowei Zhang, 2021. "Ranking and Selection with Covariates for Personalized Decision Making," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1500-1519, October.
    7. Weiwei Fan & L. Jeff Hong & Xiaowei Zhang, 2020. "Distributionally Robust Selection of the Best," Management Science, INFORMS, vol. 66(1), pages 190-208, January.
    8. Ruijing Wu & Shaoxuan Liu & Zhenyang Shi, 2019. "Customer Incentive Rebalancing Plan in Free-Float Bike-Sharing System with Limited Information," Sustainability, MDPI, vol. 11(11), pages 1-24, May.
    9. Cheng Li & Siyang Gao & Jianzhong Du, 2023. "Convergence Analysis of Stochastic Kriging-Assisted Simulation with Random Covariates," INFORMS Journal on Computing, INFORMS, vol. 35(2), pages 386-402, March.
    10. Daniel Russo, 2020. "Simple Bayesian Algorithms for Best-Arm Identification," Operations Research, INFORMS, vol. 68(6), pages 1625-1647, November.
    11. David J. Eckman & Shane G. Henderson, 2022. "Posterior-Based Stopping Rules for Bayesian Ranking-and-Selection Procedures," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1711-1728, May.
    12. L. Jeff Hong & Guangxin Jiang & Ying Zhong, 2022. "Solving Large-Scale Fixed-Budget Ranking and Selection Problems," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 2930-2949, November.

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    Keywords

    ranking and selection; parallel computing;

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