Parallel computing on heterogeneous workstation clusters has proved to be a very efficient use of available resources, increasing their overall utilization. However, for it to be a viable alternative to expensive, dedicated parallel machines, a number of key issues need to be resolved. One of the major challenges of heterogeneous computing is coping with the inherent heterogeneity of the system, with the availability of workstations from different vendors of varying processing speeds and capabilities. The existence of multiple jobs and users further complicates the task. The time taken for a parallel job is constrained by the time taken by the slowest or the most heavily loaded workstation. Therefore, load sharing of parallel computations is imperative in ensuring good overall utilization of the system. Since load sharing is essentially independent of the particular parallel job being run, the development of program independent, automatic, scheduling and load sharing strategies have become vital to the efficient use of the heterogeneous cluster. This thesis discusses various prior approaches to load sharing, examines a new strategy developed for heterogeneous workstations, and evaluates its performance
