Research has been conducted to determine how distributed computations can be mapped to multiprocessors to minimize execution time. The approach described here, known as post-game analysis, incrementally changes the program partitioning in between program execution time in subsequent runs. Post-game analysis differs from conventional iterative refinement or controlled opportunistic perturbation in that no abstract program models or any single objective function are employed to determine the relative merits of two alternative mappings. Multiple optimization subgoals are formulated, based on actual timing data gathered during program execution. Heuristics, based on various optimization subgoals, are then applied to propose changes to the current mapping. Finally, a mapping generation process which prioritizes and resolves conflicting proposals is applied. Results obtained from simulations show that post-game analysis consistently out-performs random placement, load-balancing, and clustering algorithms by 15%. Few iterations are required for simulations involving more than 200 processes and 64 sites. A rule-based architecture enables incremental strategy refinement, thus making post-game analysis easily tailorable to programs written in many concurrent programming paradigms and multiprocessor architectures.>