Beyond CPU Frequency Scaling for a Fine-grained Energy Control of HPC Systems

• Ghislain Landry Tsafack Chetsa INRIA, LIP Laboratory (UMR CNRS, ENS, INRIA, UCB), ENS Lyon, Université de Lyon
• Laurent Lefevre INRIA, LIP Laboratory (UMR CNRS, ENS, INRIA, UCB), ENS Lyon, Université de Lyon
• Jean-Marc Pierson IRIT (UMR CNRS), Université de Toulouse
• Patricia Stolf IRIT (UMR CNRS), Université de Toulouse
• Georges Da Costa IRIT (UMR CNRS), Université de Toulouse

Resumo

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.

Palavras-chave: Vectors, Hardware, Sensor phenomena and characterization, Runtime, Radiation detectors, Energy consumption, energy optimization, phase identification, hardware performance counters, system adaptation