Abstract
dc:description.abstractToday, power consumption costs supercomputer centers millions of dollars annually and the heat produced can reduce system reliability and availability. Achieving high performance while reducing power consumption is challenging since power and performance are inextricably interwoven; reducing power often results in degradation in performance. This thesis aims to address these challenges by providing theories, techniques, and tools to 1) accurately predict performance and improve it in systems with advanced hierarchical memories, 2) understand and evaluate power and its impacts on performance, 3) control power and performance for maximum efficiency. Our theories, techniques, and tools have been applied to high-end computing systems. Our theroetical models can improve algorithm performance by up to 59% and accurately predict the impacts of power on performance. Our techniques can evaluate power consumption of high-end computing systems and their applications with fine granularity and save up to 36% energy with little performance degradation.
Degree
thesis:*- Name thesis:degree_name
- Ph. D.
- Level thesis:degree_level
- doctoral
- Discipline thesis:degree_discipline
- Computer Science
- Department dc:contributor.department
- Computer Science
- Grantor dc:publisher
- Virginia Tech
- Year dc:date.issued
- 2007
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Ge, Rong
- Chair dc:contributor.committeechair
-
- Cameron, Kirk W.
- Committee members dc:contributor.committeemember
-
- Hsiao, Michael S.
- Back, Godmar V.
- Kafura, Dennis G.
- Ribbens, Calvin J.
Subjects
dc:subject × 4Rights
dc:rights- Statement dc:rights
-
- In Copyright
- Licence dc:rights.uri
Identifiers
dc:identifier.*- Dc Identifier Other
- etd-09042007-113753
- OAI identifier oai:identifier
- oai:vtechworks.lib.vt.edu:10919/28863