Beyond CPU Frequency Scaling for a Fine-grained Energy Control of HPC Systems - Chetsa, Ghislain Landry Tsafack; Lefevre, Laurent; Pierson, Jean-Marc; Stolf, Patricia; Costa, Georges Da (Anais do International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), 2012)
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.