Modern high performance computing subsystems (HPC) - including processor, network, memory, and IO - are provided with power management mechanisms. These include dynamic speed scaling and dynamic resource sleeping. Understanding the behavioral patterns of high performance computing systems at runtime can lead to a multitude of optimization opportunities including controlling and limiting their energy usage. In this paper, we present a general purpose methodology for optimizing energy performance of HPC systems considering processor, disk and network. We rely on the concept of execution vector along with a partial phase recognition technique for on-the-fly dynamic management without any a priori knowledge of the workload. We demonstrate the effectiveness of our management policy under two real-life workloads. Experimental results show that our management policy in comparison with baseline unmanaged execution saves up to 24% of energy with less than 4% performance overhead for our real-life workloads. © 2012 IEEE.
CITATION STYLE
Chetsa, G. L. T., Lefevre, L., Pierson, J. M., Stolf, P., & Da Costa, G. (2012). Beyond CPU frequency scaling for a fine-grained energy control of HPC systems. In Proceedings - Symposium on Computer Architecture and High Performance Computing (pp. 132–138). https://doi.org/10.1109/SBAC-PAD.2012.32
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