E-AMOM: an energy-aware modeling and optimization methodology for scientific applications

  • Charles Lively
  • Valerie Taylor
  • Xingfu Wu
  • Hung-Ching Chang
  • Chun-Yi Su
  • Kirk Cameron
  • Shirley Moore
  • Dan Terpstra
Special Issue Paper


In this paper, we present the Energy-Aware Modeling and Optimization Methodology (E-AMOM) framework, which develops models of runtime and power consumption based upon performance counters and uses these models to identify energy-based optimizations for scientific applications. E-AMOM utilizes predictive models to employ run-time Dynamic Voltage and Frequency Scaling (DVFS) and Dynamic Concurrency Throttling (DCT) to reduce power consumption of the scientific applications, and uses cache optimizations to further reduce runtime and energy consumption of the applications. The models and optimization are done at the level of the kernels that comprise the application. Our models resulted in an average error rate of at most 6.79 % for Hybrid MPI/OpenMP and MPI implementations of six scientific applications. With respect to optimizations, we were able to reduce the energy consumption by up to 21 %, with a reduction in runtime by up to 14.15 %, and a reduction in power consumption by up to 12.50 %.


Performance modeling Energy consumption Power consumption MPI Hybrid MPI/OpenMP Power prediction Performance optimization 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Charles Lively
    • 1
  • Valerie Taylor
    • 1
  • Xingfu Wu
    • 1
  • Hung-Ching Chang
    • 2
  • Chun-Yi Su
    • 2
  • Kirk Cameron
    • 2
  • Shirley Moore
    • 3
  • Dan Terpstra
    • 4
  1. 1.Department of Computer Science and EngineeringTexas A&M UniversityCollege StationUSA
  2. 2.Department of Computer ScienceVirginia TechBlacksburgUSA
  3. 3.Dept. of Computer ScienceUniversity of Texas at El PasoEl PasoUSA
  4. 4.Innovative Computing Lab.University of TennesseeKnoxvilleUSA

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