E-AMOM: an energy-aware modeling and optimization methodology for scientific applications
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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 %.
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- E-AMOM: an energy-aware modeling and optimization methodology for scientific applications
Computer Science - Research and Development
Volume 29, Issue 3-4 , pp 197-210
- Cover Date
- Print ISSN
- Online ISSN
- Springer Berlin Heidelberg
- Additional Links
- Performance modeling
- Energy consumption
- Power consumption
- Hybrid MPI/OpenMP
- Power prediction
- Performance optimization
- Industry Sectors
- Author Affiliations
- 1. Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA
- 2. Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
- 3. Dept. of Computer Science, University of Texas at El Paso, El Paso, TX, USA
- 4. Innovative Computing Lab., University of Tennessee, Knoxville, TN, USA