Iterative task division method or multiprocessor scheduling problem

This paper is concerned with the grain-size problem; how can we partition a program into tasks (concurrent modules) for parallel processing. Multiprocessor scheduling problem has long been studied by a number of researchers, and several scheduling algorithms have also been proposed. However, the effect of parallel processing strictly attributes of the given tasks such as the topology of task graph (directed acyclic graph representing precedence relations among tasks) and the balance of grain-size (computational time of each task) as well as the available number of processors. In order to improve the solution of multiprocessor scheduling problem, this paper proposes an excellent method for restructuring a given task graph. Analyzing the topology of the task graph, adequate tasks are divided one by one for increasing the parallelism of the task graph. The effect of task division is evaluated by scheduling tasks, where communication delays between distinct processors are considered. As a result, an appropriate task graph could be derived from the initially given task graph with respect to the designated number of processors. The proposed method is successfully applied to the parallel processing of robot-arm control computation on a multiprocessor system