Modeling Clustered Task Graphs for Scheduling Large Parallel Programs in Distributed Systems

Abstract

The recent development of distributed processing platforms, such as clusters of workstations, makes the use of distributed applications more extensive. A fundamental issue affecting performance in distributed systems is the scheduling of tasks to processors. The problem of solving the scheduling of parallel programs modelled with a task precedence graph (TPG) has been extensively studied. Scheduling algorithms of TPGs can be solved by a one-step method when a fixed number of processors is considered or through a two-step method by first creating an unbounded number of groups of tasks (clusters) and subsequently assigning these clusters to a bounded number of processors. The goal of this work is to present a new mapping algorithm called TASC (Task ASsignment exploiting Concurrency) to solve this second step of assigning clusters to processors. The effectiveness of TASC is established through simulation for a set of synthetic graphs that model real applications.