When we have sufficient computational resources to treat a simulation optimization problem as a ranking & selection (R&S) problem, then it can be "solved." R&S is exhaustive search - all feasible solutions are simulated - with meaningful statistical error control. High-performance parallel computing promises to extend the R&S limit to even larger problems, but parallelizing R&S procedures in a way that maintains statistical validity while achieving substantial speed-up is difficult. In this paper we introduce an entirely new framework for R&S called Parallel Adaptive Survivor Selection (PASS) that is specifically engineered to exploit parallel computing environments for solving simulation optimization problems with a very large number of feasible solutions.