Multiprocessor Scheduling by Simulated Evolution

This paper presents a variant of simulated evolution technique for the static non-preemptive scheduling of parallel programs represented by directed acyclic graphs including inter-processor communication delays and contention onto a multiprocessor system with the dual objectives of reducing the total execution time and scaling with the number of processors. The premise of our algorithm is Simulated Evolution, an effective optimization method based on the analogy with the natural selection process of biological evolution. The proposed technique, named Scheduling with Simulated Evolution (SES), combines simulated evolution with list scheduling, wherein simulated evolution efficiently determines suitable priorities which lead to a good solution by applying list scheduling as a decoding heuristic. SES is an effective method that yields reduced length schedules while scaling well and incurring reasonably low complexity. The SES technique does not require problem-specific parameter tuning on test problems of different sizes and structures. Moreover, it strikes a balance between exploration of the search space and exploitation of good solutions found in an acceptable CPU time. We demonstrate the effectiveness of SES by comparing it against two existing static scheduling techniques for the test examples reported in literature and on a suite of randomly generated graphs. The proposed technique produced good quality solutions with a slight increase in the CPU time as compared with the competing techniques.