Applications in science and engineering require large parallel systems in order to solve computational problems within a reasonable timeframe. These applications can benefit from dynamic resizing during the course of their execution. Dynamic resizing enables fine-grained control over resource allocation to jobs and results in better system throughput and job turn around time. We have implemented a framework that enabled dynamic resizing of MPI applications. Our framework uses the recently released MPI-2 standard that enables dynamic resizing. The work described in this thesis is part of a larger effort to design and implement a system for supporting and leveraging dynamically resizable parallel applications. We provide a scheduling framework, an API for dynamic resizing and libraries to efficiently redistribute data to new processor topologies. / Master of Science
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/41130 |
| Date | 18 February 2005 |
| Creators | Swaminathan, Gautam |
| Contributors | Computer Science, Ribbens, Calvin J., Kafura, Dennis G., Varadarajan, Srinidhi |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | GSThesis.pdf |
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