READEX - Runtime Exploitation of Application Dynamism for Energy-efficient eXascale computing
High Performance Computing (HPC) has become a major instrument for many scientific and industrial fields to generate new insights and product developments. There is a continuous demand for growing compute power, leading to a constant increase in system size and complexity. Efficiently utilizing the resources provided on Exascale systems will be a challenging task, potentially causing a large amount of underutilized resources and wasted energy. Parameters for adjusting the system to application requirements exist both on the hardware and on the system software level but are mostly unused today. Moreover, accelerators and co-processors offer a significant performance improvement at the cost of increased overhead,e.g., for data-transfers.
While HPC applications are usually highly compute intensive, they also exhibit a large degree of dynamic behaviour, e.g., the alternation between communication phases and compute kernels. Manually detecting and leveraging this dynamism to improve energy-efficiency is a tedious task that is commonly neglected by developers. However, using an automatic optimization approach, application dynamism can be detected at design-time and used to generate optimized system configurations. A light-weight run-time system will then detect this dynamic behaviour in production and switch parameter configurations if beneficial for the performance and energy-efficiency of the application. The READEX project will develop an integrated tool-suite and the READEX Programming Paradigm to exploit application domain knowledge, together achieving an improvement in energy-efficiency of up to 22.5%.
Driven by a consortium of European experts from academia, HPC resource providers, and industry, the READEX project will develop a tools-aided methodology to exploit the dynamic behaviour of applications to achieve improved energy-efficiency and performance. The developed tool-suite will be efficient and scalable to support current and future extreme scale systems.
Partners
- Technische Universität Dresden/ZIH (Coordinator)
- Norges Tekniski-Naturvitenskapelige Universitet
- Vysoka Skola Banska - Technicka Univerzita Ostrava
- National University of Ireland/Galway
- Intel Corporation SAS
- Technische Universität München
- Gesellschaft für numerische Simulation mbH
Project Website
ZIH Contact
Duration
01.09.2015 - 31.08.2018
Funding
EC (FET-PROACTIVE - TOWARDS EXASCALE HIGH PERFORMANCE COMPUTING / H2020-FETHPC-2014)
Publications
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Robert Schöne, Thomas Ilsche, Mario Bielert, Andreas Gocht, Daniel Hackenberg. "Energy Efficiency Features of the Intel Skylake-SP Processor and Their Impact on Performance" In: 6th Special Session on High Performance Computing Benchmarking and Optimization (HPBench). 2019. Accepted for publication.
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Thomas Ilsche, Robert Schöne, Mario Bielert, Andreas Gocht and Daniel Hackenberg. "lo2s – Multi-Core System and Application Performance Analysis for Linux" In: Workshop on Monitoring and Analysis for High Performance Computing Systems Plus Applications (HPCMASPA). 2017. DOI: 10.1109/CLUSTER.2017.116
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Thomas Ilsche, Marcus Hähnel, Robert Schöne, Mario Bielert and Daniel Hackenberg. "Powernightmares: The Challenge of Efficiently Using Sleep States on Multi-Core Systems" In: 5th Workshop on Runtime and Operating Systems for the Many-core Era (ROME). 2017. (accepted for publication)
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Daniel Molka, Robert Schöne, Daniel Hackenberg, Wolfgang E. Nagel. "Detecting Memory-Boundedness with Hardware Performance Counters", In: International Conference on Performance Engineering (ICPE), 2017. DOI: 10.1145/3030207.3030223
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Per Gunnar Kjeldsberg, Andreas Gocht, Michael Gerndt, Riha Lubomir, Joseph Schuchart, Umbreen Sabir Mian. "READEX: Linking Two Ends of the Computing Continuum to Improve Energy-efficiency in Dynamic Applications." In: Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017. 10.23919/DATE.2017.7926967
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Joseph Schuchart, Michael Gerndt, P.G. Kjeldsberg et al. "The READEX formalism for automatic tuning for energy efficiency" In: Computing, 2017. DOI:10.1007/s00607-016-0532-7
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R. Schöne, R. Tschüter, T. Ilsche, J. Schuchart, D. Hackenberg, and W. E. Nagel. “Extending the Functionality of Score-P through Plugins: Interfaces and Use Cases” In: Tools for High Performance Computing 2016, DOI: 10.1007/978-3-319-56702-0_4
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Robert Schöne, Thomas Ilsche, Mario Bielert, Daniel Molka, Daniel Hackenberg. "Software Controlled Clock Modulation for Energy Efficiency Optimization on Intel Processors" In: 4th International Workshop on Energy Efficient Supercomputing (E2SC). 2016. DOI: doi.org/10.1109/E2SC.2016.015
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David Horák, Lubomir Říha, Radim Sojka, Jakub Kružík, Martin Beseda, M. Cermak and Joseph Schuchart. “Energy consumption optimization of the Total-FETI solver by changing the CPU frequency” In: Proceedings from ICNAAM conference. 2016.
- Joseph Schuchart, Daniel Hackenberg, Robert Schöne, Thomas Ilsche, Ramkumar Nagappan, Michael K. Patterson. “The Shift from Processor Power Consumption to Performance Variations: Fundamental Implications at Scale.” In: First Workshop on Energy-Aware High Performance Computing (EnA-HPC). 2016. DOI: 10.1007/s00450-016-0327-2
- Y. Oleynik, Michael Gerndt, Joseph Schuchart, P. G. Kjeldsberg and Wolfgang E. Nagel, “Run-Time Exploitation of Application Dynamism for Energy-Efficient Exascale Computing (READEX)” In: 18th International Conference on Computational Science and Engineering (CSE). 2015. DOI: 10.1109/CSE.2015.55