Provided by: Ohio State University
Date Added: May 2006
Computationally complex applications can often be viewed as a collection of coarse-grained data-parallel tasks with precedence constraints. The researchers have shown that combining task and data parallelism (mixed parallelism) can be an effective approach for executing these applications, as compared to pure task or data parallelism. In this paper, the authors present an approach to determine the appropriate mix of task and data parallelism, i.e., the set of tasks that should be run concurrently and the number of processors to be allocated to each task.