Reference : Automatic Software Tuning of Parallel Programs for Energy-Aware Executions
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/42069
Automatic Software Tuning of Parallel Programs for Energy-Aware Executions
English
Varrette, Sébastien mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC)]
Pinel, Frédéric mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)]
Kieffer, Emmanuel mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)]
Danoy, Grégoire mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC)]
Bouvry, Pascal mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)]
Dec-2019
Proc. of 13th Intl. Conf. on Parallel Processing and Applied Mathematics (PPAM 2019)
Springer Verlag
LNCS
Yes
International
Bialystok
Poland
Proc. of 13th Intl. Conf. on Parallel Processing and Applied Mathematics (PPAM 2019)
September 8-11, 2019
Bialystok
Poland
[en] For large scale systems, such as data centers, energy efficiency has proven to be key for reducing capital, operational expenses and environmental impact. Power drainage of a system is closely related to the type and characteristics of workload that the device is running. For this reason, this paper presents an automatic software tuning method for parallel program generation able to adapt and exploit the hardware features available on a target computing system such as an HPC facility or a cloud system in a better way than traditional compiler infrastructures. We propose a search based approach combining both exact methods and approximated heuristics evolving programs in order to find optimized configurations relying on an ever-increasing number of tunable knobs i.e., code transformation and execution options (such as the num- ber of OpenMP threads and/or the CPU frequency settings). The main objective is to outperform the configurations generated by traditional compiling infrastructures for selected KPIs i.e., performance, energy and power usage (for both for the CPU and DRAM), as well as the runtime. First experimental results tied to the local optimization phase of the proposed framework are encouraging, demonstrating between 8% and 41% improvement for all considered metrics on a reference benchmark- ing application (i.e., Linpack). This brings novel perspectives for the global optimization step currently under investigation within the presented framework, with the ambition to pave the way toward automatic tuning of energy-aware applications beyond the performance of the current state-of-the-art compiler infrastructures.
University of Luxembourg: High Performance Computing - ULHPC
http://hdl.handle.net/10993/42069
http://www.ppam.pl/
Publication expected to appear in 2020

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