  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 $10^6$ 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.
