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Efficient Ranking And Selection In Parallel Computing Environments

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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. However, nontrivial statistical and implementation issues arise when designing R&S procedures for a parallel computing environment. This dissertation develops efficient parallel R&S procedures. In this dissertation, several design principles are proposed 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 the three parallel R&S procedures analyzed, each of which features a unique sampling and screening approach, and a specific statistical guarantee on the quality of the final solution. Finally, in our computational study we discuss three methods for implementing R&S procedures on parallel computers, namely the Message-Passing Interface (MPI), Hadoop MapReduce, and Apache Spark, and show that MPI performs the best while Spark provides good protection against core failures at the expense of a moderate drop in core utilization.

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2016-02-01

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ranking and selection; simulation optimization; parallel computing

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Henderson,Shane G.

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Martinez,Jose F.
Frazier,Peter

Degree Discipline

Operations Research

Degree Name

Ph. D., Operations Research

Degree Level

Doctor of Philosophy

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Government Document

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dissertation or thesis

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