Recent research has demonstrated that ordinal comparison, i.e., comparing relative orders of performance measures of different designs, is efficient in the comparison and selection of designs for discrete event dynamic systems. This paper is concerned with comparison and selection in concurrent simulation where a large number of sample paths under different designs are generated efficiently. Particularly, effect of correlation among simulation processes on the convergence of ordinal comparison is investigated. By using a concept of indicator process, bounds on the rate of convergence of ordinal comparison are obtained. Such bounds imply that perturbation analysis, a powerful technique for performance analysis of discrete event dynamic systems, can be advantageous for ordinal comparison. Simulation examples show that appropriate correlation can significantly increase the convergence of ordinal comparison. Furthermore, it is shown that positive quadrant dependence provides guaranteed acceleration of the convergence of ordinal comparison compared with independent simulations.