Generalized multiprocessor scheduling for directed acyclic graphs

In the 3rd Annual ACM Symposium on Parallel Algorithms and Architectures, pp. 216-228 (JuIy 1991), we presented several new results in the theory of homogeneous multiprocessor scheduling. A directed acyclic graph (DAG) of tasks was to be scheduled. Tasks were assumed to be parallelizable-as more processors are applied to a task, the time taken to compute it decreases, yielding some speedup. Because of communication, synchronization and task scheduling overheads, this speedup increases less than linearly with the number of processors applied. The optimal scheduling problem is to determine the number of processors assigned to each task, and to the task sequencing, to minimise the finishing time. Using optimal control theory, in the special case where the speedup function of each task is p^α (where p is the amount of processing power applied to the task), a closed form solution for task graphs formed from parallel and series connections was derived. This paper considerably extends these techniques for arbitrary DAGs and applies them to matrix arithmetic compilation. The optimality conditions impose nonlinear constraints on the flow of processing power from predecessors to successors, and on the finishing times of siblings. This paper presents a fast algorithm for determining and solving these nonlinear equations. The algorithm utilizes the structure of the finishing time equations to efficiently run a conjugate gradient minimization leading to the optimal solution. The algorithm has been tested on a variety of DAGs. The results presented show that it is superior to alternative heuristic approaches