Energy-aware scheduling

Optimizing performance under power constraints

Energy efficiency

Upcoming high-performance computing (HPC) systems are on the critical path towards delivering the highest level of performance for large-scale applications. If contemporary technology were used to build ever more powerful HPC systems, the power demand required by those systems would be unsustainable as it would require hundreds of megawatts of power. Thus, current HPC systems must be built considering energy efficiency as the first and foremost design goal. To achieve a sustainable power draw, future HPC systems will have to feature a power efficiency of around 50 GFlops/Watt. Such power efficiency levels require novel software/­hardware co-design, with software guiding static and dynamic power management.

Our project

Our goals are to develop methods for controlling and reducing power consumption, managing energy budgets and energy costs whilst maintaining high performance of applications and utilization levels of data center resources by using Energy-Aware Scheduling (EAS) techniques.

Our approach is based on creating models for power consumption and performance prediction and then using these models to implement EAS policies in schedulers like IBM Spectrum LSF and Kubernetes and in frameworks like Global Extensible Open Power Manager (GEOPM).

Our focus is on power and performance of complex workflows from various domains of computationl science developed at the Hartree Centre.

Publications

[1] A. Auweter, A. Bode, M. Brehm, L. Brochard, N. Hammer, H. Huber, R. Panda, F. Thomas, T. Wilde,
A Case Study of Energy Aware Scheduling on SuperMUC,”
in Proc. 29th International Supercomputing Conference “ISC 2014,” J.M. Kunkel, T. Ludwig, H.W. Meuer (Eds) Supercomputing, LNCS 8488, Springer, pp. 394-409, 2014.

[2] V. Elisseev, J. Baker, N. Morgan, L. Brochard, T. Hewitt,
Energy Aware Scheduling Study on BlueWonder,”
in Proc. IEEE 4th International Workshop on Energy Efficient Supercomputing “E2SC@SC 2016,” pp.61-68, 2016.

[3] J. Eastep, et al.
“Global Extensible Open Power Manager: A Vehicle for HPC Community Collaboration on Co-Designed Energy Management Solutions”
in Proc. International Supercomputing Conference “ISC 2017,” J. Kunkel, R. Yokota, P. Balaji, D. Keyes (Eds) High Performance Computing, LNCS 10266, Springer, pp. 394-412, 2017.

Ask the experts

Vadim Elisseev

Vadim Elisseev

Eun Kyung Lee

Eun Kyung Lee

Milos Puzovic

Milos Puzovic