The use of asymmetric multi-core processors with on-chip computational accelerators is becoming common in a variety of environments ranging from scientific computing to enterprise applications. The focus of current research has been on making efficient use of individual systems, and porting applications to asymmetric processors. The use of these asymmetric processors, like the Cell processor, in a cluster setting is the inspiration for the Cell Connector framework presented in this thesis. Cell Connector adopts a streaming approach for providing data to compute nodes with high computing potential but limited memory resources. Instead of dividing very large data sets once among computation resources, Cell Connector slices, distributes, and collects work units off of a master data held by a single large memory machine. Using this methodology, Cell Connector is able to maximize the use of limited resources and produces results that are up to 63.3\% better compared to standard non-streaming approaches. / Master of Science
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/32824 |
| Date | 10 June 2009 |
| Creators | Rose, Benjamin Aaron |
| Contributors | Computer Science, Nikolopoulos, Dimitrios S., Butt, Ali R., Lowenthal, David K. |
| 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 | main.pdf |
Page generated in 0.0076 seconds