Special Issue Paper

Computer Science - Research and Development

, Volume 29, Issue 3, pp 197-210

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

  • Charles LivelyAffiliated withDepartment of Computer Science and Engineering, Texas A&M University
  • , Valerie TaylorAffiliated withDepartment of Computer Science and Engineering, Texas A&M University
  • , Xingfu WuAffiliated withDepartment of Computer Science and Engineering, Texas A&M University Email author 
  • , Hung-Ching ChangAffiliated withDepartment of Computer Science, Virginia Tech
  • , Chun-Yi SuAffiliated withDepartment of Computer Science, Virginia Tech
  • , Kirk CameronAffiliated withDepartment of Computer Science, Virginia Tech
  • , Shirley MooreAffiliated withDept. of Computer Science, University of Texas at El Paso
  • , Dan TerpstraAffiliated withInnovative Computing Lab., University of Tennessee

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Abstract

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 %.

Keywords

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