Parallel approach for multiprocessor scheduling

The objective of this research is to propose a low-complexity static scheduling and allocation algorithm for message-passing architectures by considering factors such as communication delays, link contention, message routing and network topology. As opposed to the conventional list-scheduling approach, our technique works by first serializing the task graph and 'injecting' all the tasks to one processor. The parallel tasks are then 'bubbled up' to other processors and are inserted at appropriate time slots. The edges among the tasks are also scheduled by treating communication links between the processors as resources. The proposed approach takes into account the link contention and underlying communication routing strategy, and can self-adjust on regular as well as arbitrary network topologies. To reduce the complexity, our scheduling algorithm is itself parallelized. To our knowledge, this is the first attempt in designing a parallel algorithm for scheduling. The proposed approach implemented on an iPSC/860 hypercube, while yielding a high speedup in its execution, performs considerably better under a wide range of parameters including the task graph size, communication-to-computation ratio, and the target system topology. Comparisons are made with two other approaches.