A survey on software methods to improve the energy efficiency of parallel computing

Jin, Chao; de Supinski, Bronis R.; Abramson, David; Poxon, Heidi; DeRose, Luiz; Dinh, Minh Ngoc; Endrei, Mark; Jessup, Elizabeth R.

doi:10.1177/1094342016665471

Title: A survey on software methods to improve the energy efficiency of parallel computing

Abstract

Energy consumption is one of the top challenges for achieving the next generation of supercomputing. Codesign of hardware and software is critical for improving energy efficiency (EE) for future large-scale systems. Many architectural power-saving techniques have been developed, and most hardware components are approaching physical limits. Accordingly, parallel computing software, including both applications and systems, should exploit power-saving hardware innovations and manage efficient energy use. In addition, new power-aware parallel computing methods are essential to decrease energy usage further. This article surveys software-based methods that aim to improve EE for parallel computing. It reviews the methods that exploit the characteristics of parallel scientific applications, including load imbalance and mixed precision of floating-point (FP) calculations, to improve EE. In addition, this article summarizes widely used methods to improve power usage at different granularities, such as the whole system and per application. In particular, it describes the most important techniques to measure and to achieve energy-efficient usage of various parallel computing facilities, including processors, memories, and networks. Overall, this article reviews the state-of-the-art of energy-efficient methods for parallel computing to motivate researchers to achieve optimal parallel computing under a power budget constraint.

Authors:

Jin, Chao ^[1]; de Supinski, Bronis R. ^[2]; Abramson, David ^[3]; Poxon, Heidi ^[4]; DeRose, Luiz ^[4]; Dinh, Minh Ngoc ^[3]; Endrei, Mark ^[3]; Jessup, Elizabeth R. ^[5]

Univ. of Queensland, Brisbane, QLD (Australia); Monash Univ., Melbourne, VIC (Australia)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Univ. of Queensland, Brisbane, QLD (Australia)
Cray Inc., Saint Paul, MN (United States)
Univ. of Colorado, Boulder, CO (United States)

Publication Date:: Fri Sep 09 00:00:00 EDT 2016

Research Org.:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1812125

Report Number(s):: LLNL-JRNL-698191
Journal ID: ISSN 1094-3420; 829335

Grant/Contract Number:: AC52-07NA27344

Resource Type:: Journal Article: Accepted Manuscript

Journal Name:: International Journal of High Performance Computing Applications

Additional Journal Information:: Journal Volume: 31; Journal Issue: 6; Journal ID: ISSN 1094-3420

Publisher:: SAGE

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; Parallel computing; high performance computing; power saving; energy efficiency; auto-tuning

Citation Formats


                    Jin, Chao, de Supinski, Bronis R., Abramson, David, Poxon, Heidi, DeRose, Luiz, Dinh, Minh Ngoc, Endrei, Mark, and Jessup, Elizabeth R. A survey on software methods to improve the energy efficiency of parallel computing.  United States: N. p., 2016. 
        Web.  doi:10.1177/1094342016665471.

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                    Jin, Chao, de Supinski, Bronis R., Abramson, David, Poxon, Heidi, DeRose, Luiz, Dinh, Minh Ngoc, Endrei, Mark, & Jessup, Elizabeth R. A survey on software methods to improve the energy efficiency of parallel computing.  United States.  https://doi.org/10.1177/1094342016665471

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                    Jin, Chao, de Supinski, Bronis R., Abramson, David, Poxon, Heidi, DeRose, Luiz, Dinh, Minh Ngoc, Endrei, Mark, and Jessup, Elizabeth R. 2016.  
        "A survey on software methods to improve the energy efficiency of parallel computing".  United States.  https://doi.org/10.1177/1094342016665471.  https://www.osti.gov/servlets/purl/1812125.

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@article{osti_1812125,

  title        = {A survey on software methods to improve the energy efficiency of parallel computing},

  author       = {Jin, Chao and de Supinski, Bronis R. and Abramson, David and Poxon, Heidi and DeRose, Luiz and Dinh, Minh Ngoc and Endrei, Mark and Jessup, Elizabeth R.},

  abstractNote = {Energy consumption is one of the top challenges for achieving the next generation of supercomputing. Codesign of hardware and software is critical for improving energy efficiency (EE) for future large-scale systems. Many architectural power-saving techniques have been developed, and most hardware components are approaching physical limits. Accordingly, parallel computing software, including both applications and systems, should exploit power-saving hardware innovations and manage efficient energy use. In addition, new power-aware parallel computing methods are essential to decrease energy usage further. This article surveys software-based methods that aim to improve EE for parallel computing. It reviews the methods that exploit the characteristics of parallel scientific applications, including load imbalance and mixed precision of floating-point (FP) calculations, to improve EE. In addition, this article summarizes widely used methods to improve power usage at different granularities, such as the whole system and per application. In particular, it describes the most important techniques to measure and to achieve energy-efficient usage of various parallel computing facilities, including processors, memories, and networks. Overall, this article reviews the state-of-the-art of energy-efficient methods for parallel computing to motivate researchers to achieve optimal parallel computing under a power budget constraint.},

  doi          = {10.1177/1094342016665471},

  url          = {https://www.osti.gov/biblio/1812125},
  journal      = {International Journal of High Performance Computing Applications},

  issn         = {1094-3420},

  number       = 6,

  volume       = 31,

  place        = {United States},

  year         = {Fri Sep 09 00:00:00 EDT 2016},

  month        = {Fri Sep 09 00:00:00 EDT 2016}

}

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Energy-Aware High-Performance Computing: Survey of State-of-the-Art Tools, Techniques, and Environments
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Similar Records

Title: A survey on software methods to improve the energy efficiency of parallel computing

Abstract

Citation Formats

The International Exascale Software Project roadmap journal, January 2011

Prediction-Based Power-Performance Adaptation of Multithreaded Scientific Codes journal, October 2008

Energy-Efficient Server Clusters book, January 2003

Energy-Efficient High Performance Computing book, January 2013

On the Interplay of Parallelization, Program Performance, and Energy Consumption journal, March 2010

A Survey of Methods for Analyzing and Improving GPU Energy Efficiency journal, August 2014

Parallel job scheduling for power constrained HPC systems journal, December 2012

TUE, a New Energy-Efficiency Metric Applied at ORNL’s Jaguar book, January 2013

Minimizing Communication in Numerical Linear Algebra journal, July 2011

Active Harmony: Towards Automated Performance Tuning conference, January 2002

Measuring power consumption on IBM Blue Gene/P journal, August 2011

Power-Management Architecture of the Intel Microarchitecture Code-Named Sandy Bridge journal, March 2012

Reducing Energy Costs for IBM Blue Gene/P via Power-Aware Job Scheduling book, January 2014

Dynamic Voltage and Frequency Scaling for Scientific Applications book, January 2003

PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications journal, May 2010

Energy efficiency of mixed precision iterative refinement methods using hybrid hardware platforms: An evaluation of different solver and hardware configurations journal, August 2010

Characterization of Fixed and Reconfigurable Multi-Core Devices for Application Acceleration journal, November 2010

Energy-aware job scheduler for high-performance computing journal, August 2011

Limit to the Bit-Rate Capacity of Electrical Interconnects from the Aspect Ratio of the System Architecture journal, February 1997

Analyzing the Energy-Time Trade-Off in High-Performance Computing Applications journal, June 2007

Power Aware Dynamic Provisioning of HPC Networks report, October 2015

Wattch: a framework for architectural-level power analysis and optimizations conference, January 2000

Performance estimation of high performance computing systems with Energy Efficient Ethernet technology journal, July 2013

Self-Tuned Software-Managed Energy Reduction in InfiniBand Links conference, December 2015

Coping with Parametric Variation at Near-Threshold Voltages journal, July 2013

CALU: A Communication Optimal LU Factorization Algorithm journal, October 2011

Extending Amdahl's Law for Energy-Efficient Computing in the Many-Core Era journal, December 2008

Computer Architecture Techniques for Power-Efficiency journal, January 2008

PowerInsight - A commodity power measurement capability conference, June 2013

What is Happening to Power, Performance, and Software? journal, May 2012

On the Use of Commodity Ethernet Technology in Exascale HPC Systems conference, December 2015

Dynamic Voltage and Frequency Scaling for Scientific Applications text, January 2021

Wattch: a framework for architectural-level power analysis and optimizations journal, May 2000

Energy Efficiency of Mixed Precision Iterative Refinement Methods using Hybrid Hardware Platforms: An Evaluation of different Solver and Hardware Configurations text, January 2010

Energy-Aware High-Performance Computing: Survey of State-of-the-Art Tools, Techniques, and Environments journal, April 2019

Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review journal, April 2018

A Taxonomy and Future Directions for Sustainable Cloud Computing: 360 Degree View journal, January 2019

Statistical and machine learning models for optimizing energy in parallel applications journal, April 2019

A Taxonomy and Future Directions for Sustainable Cloud Computing: 360 Degree View preprint, January 2017

Using Meta-heuristics and Machine Learning for Software Optimization of Parallel Computing Systems: A Systematic Literature Review text, January 2018

The International Exascale Software Project roadmap
journal, January 2011

Prediction-Based Power-Performance Adaptation of Multithreaded Scientific Codes
journal, October 2008

Energy-Efficient Server Clusters
book, January 2003

Energy-Efficient High Performance Computing
book, January 2013

On the Interplay of Parallelization, Program Performance, and Energy Consumption
journal, March 2010

A Survey of Methods for Analyzing and Improving GPU Energy Efficiency
journal, August 2014

Parallel job scheduling for power constrained HPC systems
journal, December 2012

TUE, a New Energy-Efficiency Metric Applied at ORNL’s Jaguar
book, January 2013

Minimizing Communication in Numerical Linear Algebra
journal, July 2011

Active Harmony: Towards Automated Performance Tuning
conference, January 2002

Measuring power consumption on IBM Blue Gene/P
journal, August 2011

Power-Management Architecture of the Intel Microarchitecture Code-Named Sandy Bridge
journal, March 2012

Reducing Energy Costs for IBM Blue Gene/P via Power-Aware Job Scheduling
book, January 2014

Dynamic Voltage and Frequency Scaling for Scientific Applications
book, January 2003

PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications
journal, May 2010

Energy efficiency of mixed precision iterative refinement methods using hybrid hardware platforms: An evaluation of different solver and hardware configurations
journal, August 2010

Characterization of Fixed and Reconfigurable Multi-Core Devices for Application Acceleration
journal, November 2010

Energy-aware job scheduler for high-performance computing
journal, August 2011

Limit to the Bit-Rate Capacity of Electrical Interconnects from the Aspect Ratio of the System Architecture
journal, February 1997

Analyzing the Energy-Time Trade-Off in High-Performance Computing Applications
journal, June 2007

Power Aware Dynamic Provisioning of HPC Networks
report, October 2015

Wattch: a framework for architectural-level power analysis and optimizations
conference, January 2000

Performance estimation of high performance computing systems with Energy Efficient Ethernet technology
journal, July 2013

Self-Tuned Software-Managed Energy Reduction in InfiniBand Links
conference, December 2015

Coping with Parametric Variation at Near-Threshold Voltages
journal, July 2013

CALU: A Communication Optimal LU Factorization Algorithm
journal, October 2011

Extending Amdahl's Law for Energy-Efficient Computing in the Many-Core Era
journal, December 2008

Computer Architecture Techniques for Power-Efficiency
journal, January 2008

PowerInsight - A commodity power measurement capability
conference, June 2013

What is Happening to Power, Performance, and Software?
journal, May 2012

On the Use of Commodity Ethernet Technology in Exascale HPC Systems
conference, December 2015

Dynamic Voltage and Frequency Scaling for Scientific Applications
text, January 2021

Wattch: a framework for architectural-level power analysis and optimizations
journal, May 2000

Energy Efficiency of Mixed Precision Iterative Refinement Methods using Hybrid Hardware Platforms: An Evaluation of different Solver and Hardware Configurations
text, January 2010

Energy-Aware High-Performance Computing: Survey of State-of-the-Art Tools, Techniques, and Environments
journal, April 2019

Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review
journal, April 2018

A Taxonomy and Future Directions for Sustainable Cloud Computing: 360 Degree View
journal, January 2019

Statistical and machine learning models for optimizing energy in parallel applications
journal, April 2019

A Taxonomy and Future Directions for Sustainable Cloud Computing: 360 Degree View
preprint, January 2017

Using Meta-heuristics and Machine Learning for Software Optimization of Parallel Computing Systems: A Systematic Literature Review
text, January 2018