Date of Award
Spring 5-2010
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Computing
School
Computing Sciences and Computer Engineering
Committee Chair
Benjamin Seyfarth
Committee Chair Department
Computing
Committee Member 2
Arlene Perkins
Committee Member 2 Department
Computing
Committee Member 3
Andrew Strezoff
Committee Member 3 Department
Computing
Committee Member 4
Zheng Sun
Committee Member 4 Department
Computing
Abstract
Most programs can be parallelized to some extent. The processing power available in computers today makes parallel computing more desirable and attainable than ever before. Many machines today have multiple processors or multiple processing cores making parallel computing more available locally, as well as over a network. In order for parallel applications to be written, they require a computing language, such as C++, and a coordination language (or library), such as Linda. This research involves the creation and implementation of a coordination framework, Guppie, which is easy to use, similar to Linda, but provides more efficiency when dealing with large amounts of messages and data. Greater efficiency can be achieved in coarse-grained parallel computing through the use of shared memory managed through a master-worker relationship.
Copyright
2010, Sean Christopher McCarthy
Recommended Citation
McCarthy, Sean Christopher, "Guppie: A Coordination Framework for Parallel Processing Using Shared Memory Featuring A Master-Worker Relationship" (2010). Dissertations. 935.
https://aquila.usm.edu/dissertations/935