Title page for ETD etd-07092009-200715

Type of Document Master's Thesis
Author Cao, Zhenwei
Author's Email Address zhenwei@vt.edu
URN etd-07092009-200715
Title Power Saving Analysis and Experiments for Large Scale Global Optimization
Degree Master of Science
Department Computer Science
Advisory Committee
Advisor Name Title
Watson, Layne T. Committee Chair
Cameron, Kirk W. Committee Member
Feng, Wu-Chun Committee Member
  • power aware computing
  • high performance computing
  • DVFS
  • large scale global optimization
  • budding yeast problem
Date of Defense 2009-07-06
Availability unrestricted
Green computing, an emerging field of research that seeks to reduce excess power consumption in high performance computing (HPC), is gaining popularity among researchers. Research in this field often relies on simulation or only uses a small cluster, typically 8 or 16 nodes, because of the lack of hardware support. In contrast, System G at Virginia Tech is a 2592 processor supercomputer equipped with power aware components suitable for large scale green computing research. DIRECT is a deterministic global optimization algorithm, implemented in the mathematical software package VTDIRECT95. This thesis explores the potential energy savings for the parallel implementation of DIRECT, called pVTdirect, when used with a large scale computational biology application, parameter estimation for a budding yeast cell cycle model, on System G. Two power aware approaches for pVTdirect are developed and compared against the CPUSPEED power saving system tool. The results show that knowledge of the parallel workload of the underlying application is beneficial for power management.
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