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STEAM: A Smart Temperature and Energy Aware Multicore Controller

Published: 06 October 2014 Publication History

Abstract

Recent empirical studies have shown that multicore scaling is fast becoming power limited, and consequently, an increasing fraction of a multicore processor has to be under clocked or powered off. Therefore, in addition to fundamental innovations in architecture, compilers and parallelization of application programs, there is a need to develop practical and effective dynamic energy management (DEM) techniques for multicore processors.
Existing DEM techniques mainly target reducing processor power consumption and temperature, and only few of them have addressed improving energy efficiency for multicore systems. With energy efficiency taking a center stage in all aspects of computing, the focus of the DEM needs to be on finding practical methods to maximize processor efficiency. Towards this, this article presents STEAM -- an optimal closed-loop DEM controller designed for multicore processors. The objective is to maximize energy efficiency by dynamic voltage and frequency scaling (DVFS). Energy efficiency is defined as the ratio of performance to power consumption or performance-per-watt (PPW). This is the same as the number of instructions executed per Joule. The PPW metric is actually replaced by PαPW (performanceα-per-Watt), which allows for controlling the importance of performance versus power consumption by varying α.
The proposed controller was implemented on a Linux system and tested with the Intel Sandy Bridge processor. There are three power management schemes called governors, available with Intel platforms. They are referred to as (1) Powersave (lowest power consumption), (2) Performance (achieves highest performance), and (3) Ondemand. Our simple and lightweight controller when executing SPEC CPU2006, PARSEC, and MiBench benchmarks have achieved an average of 18% improvement in energy efficiency (MIPS/Watt) over these ACPI policies. Moreover, STEAM also demonstrated an excellent prediction of core temperatures and power consumption, and the ability to control the core temperatures within 3ˆC of the specified maximum. Finally, the overhead of the STEAM implementation (in terms of CPU resources) is less than 0.25%. The entire implementation is self-contained and can be installed on any processor with very little prior knowledge of the processor.

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Published In

cover image ACM Transactions on Embedded Computing Systems
ACM Transactions on Embedded Computing Systems  Volume 13, Issue 5s
Special Issue on Risk and Trust in Embedded Critical Systems, Special Issue on Real-Time, Embedded and Cyber-Physical Systems, Special Issue on Virtual Prototyping of Parallel and Embedded Systems (ViPES)
November 2014
501 pages
ISSN:1539-9087
EISSN:1558-3465
DOI:10.1145/2660459
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 06 October 2014
Accepted: 01 April 2014
Revised: 01 February 2014
Received: 01 June 2013
Published in TECS Volume 13, Issue 5s

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Author Tags

  1. Kalman filter
  2. Multicore
  3. closed-loop controller
  4. dynamic energy management
  5. dynamic thermal management
  6. dynamic voltage and frequency scaling
  7. energy efficiency
  8. leakage power dependence on temperature
  9. least-squares estimation
  10. performance optimal
  11. power and temperature modeling

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  • (2023)Dynamic power budget redistribution under a power cap on multi-application environmentsSustainable Computing: Informatics and Systems10.1016/j.suscom.2023.10086538(100865)Online publication date: Apr-2023
  • (2022)Applying Game-Learning Environments to Power Capping Scenarios via Reinforcement LearningCloud Computing, Big Data & Emerging Topics10.1007/978-3-031-14599-5_7(91-106)Online publication date: 5-Aug-2022
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  • (2019)TangramProceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture10.1145/3352460.3358285(384-398)Online publication date: 12-Oct-2019
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