Ranking-and-selection (R&S) procedures find the best from a finite set of simulated alternatives with a statistical guarantee of correct selection.  The availability of inexpensive parallel computing, such as multi-core personal computers and many-core servers, is turning many simulation optimization problems into ranking-and-selection (R&S) problems because we can afford to simulate all of the (perhaps tens of thousands of) alternatives. However, most R&S procedures were designed to be implemented on a single processor, and, as we demonstrate, naïve application in a parallel environment can make them invalid. We propose two types of fully sequential procedures for use in parallel computing environments: a vector-filling procedure and an asymptotically valid parallel sequential procedure. Numerical experiments show that the proposed procedures can take advantage of multiple parallel processors and solve large-scale R&S problems.