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PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
BEGIN:VEVENT
DTSTART:20121114T181500Z
DTEND:20121114T183000Z
LOCATION:155-F
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: Meeting energy and power requirements for huge exascale machines is a major challenge. Energy costs for data centers can be divided into two main categories: machine energy and cooling energy=0Aconsumptions. This thesis investigates reduction in energy consumption for HPC data centers in both these categories. Our recent work on reducing cooling energy consumption shows that we can reduce cooling energy consumption by up to 63% using our temperature aware load balancer. In this work, we also demonstrate that data centers can reduce machine energy consumption by up to 28% by running different parts of the applications at different frequencies. The focus of our work is to gauge the potential for energy saving by reducing both machine and cooling energy consumption separately (and their associated execution time penalty) and then come up with a scheme that combines them optimally to reduce total energy consumption for large HPC data centers.
SUMMARY:Total Energy Optimization for High Performance Computing Data Centers
PRIORITY:3
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