A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription.For more information, please contact the WRAP Team at: wrap@warwick.ac.uk Metrics for Energy-Aware Software OptimisationStephen I. Roberts, Steven A. Wright, Suhaib A. Fahmy, and Stephen A. Jarvis University of Warwick, U.K. S.I.Roberts@warwick.ac.uk Abstract. Energy consumption is rapidly becoming a limiting factor in scientific computing. As a result, hardware manufacturers increasingly prioritise energy efficiency in their processor designs. Performance engineers are also beginning to explore software optimisation and hardware/software co-design as a means to reduce energy consumption. Energy efficiency metrics developed by the hardware community are often re-purposed to guide these software optimisation efforts. In this paper we argue that established metrics, and in particular those in the Energy Delay Product (Et n ) family, are unsuitable for energy-aware software optimisation. A good metric should provide meaningful values for a single experiment, allow fair comparison between experiments, and drive optimisation in a sensible direction. We show that Et n metrics are unable to fulfil these basic requirements and present suitable alternatives for guiding energy-aware software optimisation. We finish with a practical demonstration of the utility of our proposed metrics.
Performance engineers are beginning to explore software-level optimisation as a means to reduce the energy consumed when running their codes. This paper presents POSE, a mathematical and visual modelling tool which highlights the relationship between runtime and power consumption. POSE allows developers to assess whether power optimisation is worth pursuing for their codes.We demonstrate POSE by studying the power optimisation characteristics of applications from the Mantevo and Rodinia benchmark suites. We show that LavaMD has the most scope for CPU power optimisation, with improvements in Energy Delay Squared Product (ED 2 P) of up to 30.59%. Conversely, MiniMD offers the least scope, with improvements to the same metric limited to 7.60%. We also show that no power optimised version of MiniMD operating below 2.3 GHz can match the ED 2 P performance of the original code running at 3.2 GHz. For LavaMD this limit is marginally less restrictive at 2.2 GHz.
Advances in processor design have delivered performance improvements for decades. As physical limits are reached, refinements to the same basic technologies are beginning to yield diminishing returns. Unsustainable increases in energy consumption are forcing hardware manufacturers to prioritise energy efficiency in their designs. Research suggests that software modifications may be needed to exploit the resulting improvements in current and future hardware. New tools are required to capitalise on this new class of optimisation. In this article, we present the Power Optimised Software Envelope (POSE) model, which allows developers to assess the potential benefits of power optimisation for their applications. The POSE model is metric agnostic and in this article, we provide derivations using the established Energy-Delay Product metric and the novel Energy-Delay Sum and Energy-Delay Distance metrics that we believe are more appropriate for energy-aware optimisation efforts. We demonstrate POSE on three platforms by studying the optimisation characteristics of applications from the Mantevo benchmark suite. Our results show that the Pathfinder application has very little scope for power optimisation while TeaLeaf has the most, with all other applications in the benchmark suite falling between the two. Finally, we extend our POSE model with a formulation known as System Summary POSE-a meta-heuristic that allows developers to assess the scope a system has for energy-aware software optimisation independent of the code being run. CCS Concepts: • Computing methodologies → Modeling and simulation; Model development and analysis; Modeling methodologies; • Hardware → Power and energy; Power estimation and optimization;
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.