Partitioning and Scheduling Using Graph Decomposition

Automated parallelization of source code is a goal on which many researchers in parallel computing have focused. The increasing availability of parallel computers, the difficulty of creating good parallel programs, and the vast amount of existing serial source code all contribute to the need for automated means of parallelization. This paper centers on the issues of partitioning and scheduling within automatic parallelization, or the creation of appropriately-sized tasks and their assignments to processors. An algorithm is introduced which uses the program dependence graph (PDG) representation of serial programs, and relies on a prior graph decomposition, or parse, for identification of parallelism. The algorithm uses local heuristics to determine the cost effectiveness of each opportunity for parallelization, and creates and schedules tasks accordingly.