Energy efficiency is a key design principle of the IBM Blue Gene series of supercomputers, and Blue Gene systems have consistently gained top GFlops/Watt rankings on the Green500 list. The Blue Gene hardware and management software provide built-in features to monitor power consumption at all levels of the machine's power distribution network. This paper presents the Blue Gene/P power measurement infrastructure and discusses the operational aspects of using this infrastructure on Petascale machines. We also describe the integration of Blue Gene power monitoring capabilities into system-level tools like LLview, and highlight some results of analyzing the production workload at Research Center Jülich (FZJ).

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Energy consumption optimization of HPC applications inherently requires measurements for reference and comparison. However, most of today's systems lack the necessary hardware support for power or energy measurements. Furthermore, in-band data availability is preferred for specific optimization techniques such as auto-tuning. For this reason, we present in-band energy consumption models for the IBM POWER7 processor based on hardware counters. We demonstrate that linear regression is a suitable means for modeling energy consumption, and we rely on already available, highlevel benchmarks for training instead of self-written or handtuned micro-kernels. We compare modeling efforts for different instruction mixes caused by two compilers (GCC and IBM XL) as well as various multi-threading usage scenarios, and validate across our training benchmarks and two real-world applications. Results show mean errors of approximately 1% and overall max errors of 5.3% for GCC.

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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 poweraware 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.