Data dependent loop scheduling based on genetic algorithms for distributed and shared memory systems

Many approaches have been described for the parallel loop scheduling problem for shared-memory systems, but little work has been done on the data-dependent loop scheduling problem (nested loops with loop carried dependencies). In this paper, we propose a general model for the data-dependent loop scheduling problem on distributed as well as shared memory systems. In order to achieve load balancing and low runtime scheduling and communication overhead, our model is based on a loop task graph and the notion of critical path. In addition, we develop a heuristic algorithm based on our model and on genetic algorithms to test the reliability of the model. We test our approach on different scenarios and benchmarks. The results are very encouraging and suggest a future parallel compiler implementation based on our model.