An approach for finding the optimal configuration of heterogeneous computer systems to solve super computing problems is presented. Superconcurrency as a form of distributed heterogeneous supercomputing is an approach for matching and managing an optimally configured suite of super-speed machines to minimize the execution time on a given task. The approach performs best when the computational requirements for a given set of tasks are diverse. A supercomputing application task is decomposed into a collection of code segments, where the processing requirement is homogeneous in each code segment. The optimal selection theory has been proposed to choose the optimal configuration of machines for a supercomputing problem. This technique is based on code profiling and analytical benchmarking. Here, the previously presented optimal selection theory approach is augmented in two ways: the performance of code segments on non-optimal machine choices is incorporated and non-uniform decompositions of code segments are considered.