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MapRedRnS

swMATH ID: 23650
Software Authors: Ni, Eric C., Dragos F. Ciocan, Susan R. Hunter, Shane G. Henderson
Description: MapRedRnS: Parallel ranking and selection using MapReduce. This is a MapReduce implementation of the GSP procedure for solving Ranking and Selection problems. The procedure is described in detail in the working paper: Ni, Eric C., Dragos F. Ciocan, Susan R. Hunter, Shane G. Henderson (2015), ”Efficient Ranking and Selection in Parallel Computing Environments”: 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. Thus 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) runs efficiently on up to 1,024 parallel cores, and (iii) in an example uses smaller sample sizes compared to existing parallel procedures, particularly for large problems (over 106 alternatives). In our computational study we discuss two methods for implementing GSP on parallel computers, namely the Message-Passing Interface (MPI) and Hadoop MapReduce and show that the latter provides good protection against core failures at the expense of a significant drop in utilization due to periodic unavoidable synchronization.
Homepage: https://bitbucket.org/ericni/mapredrns
Related Software: RngStreams; Spark; SparkRnS; MPIRNS; SimOpt; RngStreams; Hadoop; RngSteam; SPRNG; MapReduce
Cited in: 1 Document

Cited in 1 Serial

1 Operations Research

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