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Energy Efficient High Performance ProcessorsDynamic Optimizations for Energy Efficiency

Energy Efficient High Performance Processors: Dynamic Optimizations for Energy Efficiency [The growing adoption of mobile devices powered by batteries along with the high-power costs in data centers raises the need for energy-efficient computing. Dynamic voltage and frequency scaling is often used by the operating system to balance power performance. However, optimizing for energy efficiency faces multiple challenges such as when dealing with nonsteady state workloads. In this work, we develop DOEE—a novel method that optimizes certain processor features for energy efficiency using user-supplied metrics. The optimization is dynamic, taking into account the runtime characteristics of the workload and the platform. The method instruments monitoring code to search for per-program-phase optimal feature configurations that ultimately improve system energy efficiency. We demonstrate the framework using the LLVM compiler when tuning the Turbo Boost feature on modern Intel Core processors. This implementation improves energy efficiency by up to 23% on SPEC CPU2006 benchmarks, outperforming the energy-efficient firmware algorithm. ] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Energy Efficient High Performance ProcessorsDynamic Optimizations for Energy Efficiency

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Publisher
Springer Singapore
Copyright
© Springer Nature Singapore Pte Ltd. 2018
ISBN
978-981-10-8553-6
Pages
57 –72
DOI
10.1007/978-981-10-8554-3_2
Publisher site
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Abstract

[The growing adoption of mobile devices powered by batteries along with the high-power costs in data centers raises the need for energy-efficient computing. Dynamic voltage and frequency scaling is often used by the operating system to balance power performance. However, optimizing for energy efficiency faces multiple challenges such as when dealing with nonsteady state workloads. In this work, we develop DOEE—a novel method that optimizes certain processor features for energy efficiency using user-supplied metrics. The optimization is dynamic, taking into account the runtime characteristics of the workload and the platform. The method instruments monitoring code to search for per-program-phase optimal feature configurations that ultimately improve system energy efficiency. We demonstrate the framework using the LLVM compiler when tuning the Turbo Boost feature on modern Intel Core processors. This implementation improves energy efficiency by up to 23% on SPEC CPU2006 benchmarks, outperforming the energy-efficient firmware algorithm. ]

Published: Mar 23, 2018

Keywords: Dynamic Voltage And Frequency Scaling (DVFS); Turbo Boost; LLVM Compiler; Low Level Virtual Machine (LLVM); Running Average Power Limit (RAPL)

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