Exploiting concurrency among tasks in partitionable parallel processing systems
Date
1992
Authors
Siegel, Howard Jay, author
Maciejewski, Anthony A., author
Nation, Wayne G., author
IEEE, publisher
Journal Title
Journal ISSN
Volume Title
Abstract
One benefit of partitionable parallel processing systems is their ability to execute multiple independent tasks simultaneously. Previous work has identified conditions such that, when there are k tasks to be processed, partitioning the system such that all k tasks are processed simultaneously results in a minimum overall execution time. An alternate condition is developed that provides additional insight into the effects of parallelism on execution time. This result, and previous results, however, assume that execution times are data independent. It will be shown that data-dependent tasks do not necessarily execute faster when processed simultaneously even if the condition is met. A model is developed that provides for the possible variability of a task's execution time and is used in a new framework to study the problem of finding an optimal mapping for identical, independent data-dependent execution time tasks onto partitionable systems. Extension of this framework to situations where the k tasks are non-identical is discussed.
Description
Rights Access
Subject
parallel architectures
parallel algorithms
concurrency control
computational complexity