Workload execution strategies and parallel speedup on clustered computers

A model of system performance for parallel processing on clustered multiprocessors is developed which unifies multiprogramming with speedup and scaled-speedup. The model is used to explore processor to process allocation alternatives for executing a workload consisting of multiple processes. Heuristics are developed that relate cluster size to parallel fraction of a program and to process scaling factors. The basic analytical model is made more sophisticated by incorporating considerations that affect the realizable speedup, including explicit process scaling, Degree of Parallelism (DOP) as a discrete function, and communication overhead. New developments incorporate nonuniform workload, interconnection network probability of acceptance of requests, nonuniform memory access, and multithreaded processes.