Scheduling of dynamic divide-and-conquer computations on multicomputers

The scheduling of tasks for applications with dynamic behavior traditionally rely on externally observable met-rics such as the number of active processes. This paper presents a new scheduling strategy based on the observation that it may be possible to capture the near-term resource requirements of active tasks by expressions involving task parameters. Run-time evaluation of these expressions yields estimates of task behavior that are valid over a short, future interval of time. The heuristics proposed, which when used in conjunction with information supplied by prooling, can be used to annotate the source program with such expressions. Preliminary simulation results show that the use of near-future estimates in a dynamic scheduling strategy for divide-and-conquer algorithms consistently improves over traditional dynamic strategies. The performance of this strategy approaches that of the best-known deterministic strategy while incurring an overhead of the same order as other dynamic strategies.