Energy consumption has become a major topic in high performance computing in the last years. We present an approach to efficiently manage the power states of an power-aware cluster, including the processor, the network cards and the disks. To profit from the lower power consumption of these states we followed the approach to transfer application knowledge (e.g. future hardware use) to a daemon which efficiently manages the hardware device states per cluster node. After introducing our measurement environment we evaluated the general power saving potential of our AMD and Intel computing nodes. Two example high performance applications are showcases for an initial instrumentation which results in a reduction of the Energy-to-Solution between 4 and 8% with slight increases of the Time-to-Solution.

@inproceedings{MHPSMFHPCM11,
	author	 = {Timo Minartz and Michael Knobloch and Thomas Ludwig and Bernd Mohr},
	title	 = {{Managing Hardware Power Saving Modes for High Performance Computing}},
	year	 = {2011},
	booktitle	 = {{Green Computing Conference and Workshops (IGCC), 2011 International}},
	editor	 = {},
	pages	 = {1--8},
	conference	 = {IGCC},
	location	 = {Orlando, Florida, USA},
	isbn	 = {978-1-4577-1222-7},
	doi	 = {http://dx.doi.org/10.1109/IGCC.2011.6008581},
	abstract	 = {Energy consumption has become a major topic in high performance computing in the last years. We present an approach to efficiently manage the power states of an power-aware cluster, including the processor, the network cards and the disks. To profit from the lower power consumption of these states we followed the approach to transfer application knowledge (e.g. future hardware use) to a daemon which efficiently manages the hardware device states per cluster node. After introducing our measurement environment we evaluated the general power saving potential of our AMD and Intel computing nodes. Two example high performance applications are showcases for an initial instrumentation which results in a reduction of the Energy-to-Solution between 4 and 8\% with slight increases of the Time-to-Solution.},
}