Energy Efficiency: From Hardware to Software
Energy efficiency is our contribution to a sustainable future. We not only look at the efficiency of processors but also on how to operate them energy optimal. But what is energy efficiency without measurements?
Data-Dependent Power Consumption
Did you know? The power consumption of an Intel Skylake-SP processor depends not only the C-States, frequencies and used instructions but also on the data processed.
For example, computing an XOR using AVX-512 on all zeros or all ones makes a difference of 58 W. 
READEX: Optimizing the Energy Consumption of your HPC Application
Did you know? The energy consumption of an application can be reduced without sacrificing too much performance.
Simply adapt the core- and uncore frequency of the processor to your application’s needs. For the Kripke mini-app, this leads to a reduction of the energy consumption up to 15% .
Metric-Q: A Scalable Infrastructure for your High-Resolution Time Series Data
Did you know? You can access your time series data with O(1) instead of O(N) without sacrificing any information.
While MetricQ helps you to collect, analyze and save your sensor data fast, the Hierarchical Timeline Aggregation (HTA) requires moderate data processing during data collection while allowing to reduce the complexity of a typical timeline request.
Using this advanced technique, you can visualize billions of data points, e.g., from your power measurements, in an instant .
 Robert Schöne, Thomas Ilsche, Mario Bielert, Andreas Gocht and Daniel Hackenberg:
Energy Efficiency Features of the Intel Skylake-SP Processor and Their Impact on Performance.
In: 2019 International Conference on High Performance Computing & Simulation (HPCS)
DOI: 10.1109/HPCS48598.2019.9188239 [PDF]
Q-Learning Inspired Self-Tuning for Energy Efficiency in HPC
2019 International Conference on High Performance Computing & Simulation (HPCS)
 Thomas Ilsche, Daniel Hackenberg, Robert Schöne, Mario Bielert, Franz Höpfner and Wolfgang E. Nagel:
MetricQ: A Scalable Infrastructure for Processing High-Resolution Time Series Data
2019 IEEE/ACM Industry/University Joint International Workshop on Data-center Automation, Analytics, and Control (DAAC)
DOI 10.1109/DAAC49578.2019.00007 [PDF]