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 %.
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Acknowledgements
This work is supported by NSF grants CNS-0911023, CNS-0910899, CNS-0910784, CNS-0905187. The authors would like to acknowledge Stephane Ethier from Princeton Plasma Physics Laboratory for providing the GTC code, and Chee Wai Lee for his review comments.
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Lively, C., Taylor, V., Wu, X. et al. E-AMOM: an energy-aware modeling and optimization methodology for scientific applications. Comput Sci Res Dev 29, 197–210 (2014). https://doi.org/10.1007/s00450-013-0239-3
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DOI: https://doi.org/10.1007/s00450-013-0239-3