An overview of the BlueGene/L Supercomputer
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
The design, implementation, and evaluation of a compiler algorithm for CPU energy reduction
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
Conserving disk energy in network servers
ICS '03 Proceedings of the 17th annual international conference on Supercomputing
The case for power management in web servers
Power aware computing
The Bladed Beowulf: A Cost-Effective Alternative to Traditional Beowulfs
CLUSTER '02 Proceedings of the IEEE International Conference on Cluster Computing
Computer Architecture: A Quantitative Approach
Computer Architecture: A Quantitative Approach
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Online strategies for high-performance power-aware thread execution on emerging multiprocessors
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Making a case for a green500 list
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Evaluating Parallel I/O Energy Efficiency
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Reducing energy usage with memory and computation-aware dynamic frequency scaling
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
Towards efficient supercomputing: searching for the right efficiency metric
ICPE '12 Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering
Energy based performance tuning for large scale high performance computing systems
Proceedings of the 2012 Symposium on High Performance Computing
An overview of energy efficiency techniques in cluster computing systems
Cluster Computing
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Left unchecked, the fundamental drive to increase peak performance using tens of thousands of power hungry components will lead to intolerable operating costs and failure rates. Recent work has shown application characteristics of single-processor, memorybound non-interactive codes and distributed, interactive web services can be exploited to conserve power and energy with minimal performance impact. Our novel approach is to exploit parallel performance inefficiencies characteristic of non-interactive, distributed scientific applications, conserving energy using DVS (dynamic voltage scaling) without impacting time-to-solution (TTS) significantly, reducing cost and improving reliability. We present a software framework to analyze and optimize distributed power-performance using DVS implemented on a 16-node Centrino-based cluster. Using various DVS strategies we achieve application-dependent overall system energy savings as large as 25% with as little as 2% performance impact.