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Publication . Conference object . Part of book or chapter of book . 2017

Towards fine-grained dynamic tuning of HPC applications on modern multi-core architectures

Mohammed Sourouri; Espen Birger Raknes; Nico Reissmann; Johannes Langguth; Daniel Hackenberg; Robert Schöne; Per Gunnar Kjeldsberg;
Open Access
Published: 12 Nov 2017
Country: Norway
There is a consensus that exascale systems should operate within a power envelope of 20MW. Consequently, energy conservation is still considered as the most crucial constraint if such systems are to be realized. So far, most research on this topic has focused on strategies such as power capping and dynamic power management. Although these approaches can reduce power consumption, we believe that they might not be sufficient to reach the exascale energy-efficiency goals. Hence, we aim to adopt techniques from embedded systems, where energy-efficiency has always been the fundamental objective. A successful energy-saving technique used in embedded systems is to integrate fine-grained autotuning with dynamic voltage and frequency scaling. In this paper, we apply a similar technique to a real-world HPC application. Our experimental results on a HPC cluster indicate that such an approach can save up to 19% of energy compared to the baseline configuration, with negligible performance loss. © ACM, 2017. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis,
Subjects by Vocabulary

Microsoft Academic Graph classification: Computer science Efficient energy use Parallel computing Frequency scaling Energy (signal processing) Energy conservation Supercomputer Distributed computing Constraint (computer-aided design) Multi-core processor Power (physics)

Funded by
Runtime Exploitation of Application Dynamism for Energy-efficient eXascale computing
  • Funder: European Commission (EC)
  • Project Code: 671657
  • Funding stream: H2020 | RIA
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