A Power-Aware Run-Time System for High-Performance Computing
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
A comprehensive model of the supercomputer workload
WWC '01 Proceedings of the Workload Characterization, 2001. WWC-4. 2001 IEEE International Workshop
Power provisioning for a warehouse-sized computer
Proceedings of the 34th annual international symposium on Computer architecture
Analyzing the Energy-Time Trade-Off in High-Performance Computing Applications
IEEE Transactions on Parallel and Distributed Systems
PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications
IEEE Transactions on Parallel and Distributed Systems
A comparison of high-level full-system power models
HotPower'08 Proceedings of the 2008 conference on Power aware computing and systems
A survey of the research on power management techniques for high-performance systems
Software—Practice & Experience
Are user runtime estimates inherently inaccurate?
JSSPP'04 Proceedings of the 10th international conference on Job Scheduling Strategies for Parallel Processing
A methodology to predict the power consumption of servers in data centres
Proceedings of the 2nd International Conference on Energy-Efficient Computing and Networking
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In recent years energy-aware computing has become a major topic, not only in wireless and mobile devices but also in devices using wired technology. The ICT industry is consuming an increasing amount of energy and a large part of the consumption is generated by large-scale data centers. In High-Performance Computing (HPC) data centers, higher performance equals higher energy consumption. This has created incentives on exploring several alternatives to reduce the energy consumption of the system, such as energy-efficient hardware or the Dynamic Voltage and Frequency Scaling (DVFS) technique. This work presents an energy-aware scheduler that can be applied to a HPC data center without any changes in hardware. The scheduler is evaluated with a simulation model and a real-world HPC testbed. Our experiments indicate that the scheduler is able to reduce the energy consumption by 6---16% depending on the job workload. More importantly, there is no significant slowdown in the turnaround time or increase in the wait time of the job. The results hereby evidence that our approach can be beneficial for HPC data center operators without a large penalty on service level agreements.