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Autotuning based on frequency scaling toward energy efficiency of blockchain algorithms on graphics processing units

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00005098
URN to cite this document: urn:nbn:de:bvb:703-epub-5098-6

Title data

Stachowski, Matthias ; Fiebig, Alexander ; Rauber, Thomas:
Autotuning based on frequency scaling toward energy efficiency of blockchain algorithms on graphics processing units.
In: The Journal of Supercomputing. (2 April 2020) .
ISSN 1573-0484
DOI der Verlagsversion: https://doi.org/10.1007/s11227-020-03263-5

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Abstract

Energy-efficient computing is especially important in the field of high-performance computing (HPC) on supercomputers. Therefore, automated optimization of energy efficiency during the execution of a compute-intensive program is desirable. In this article, a framework for the automatic improvement of the energy efficiency on NVIDIA GPUs (graphics processing units) using dynamic voltage and frequency scaling is presented. As application, the mining of crypto-currencies is used, since in this area energy efficiency is of particular importance. The framework first determines the energy-optimal frequencies for each available currency on each GPU of a computer automatically. Then, the mining is started, and during a monitoring phase it is ensured that always the most profitable currency is mined on each GPU, using optimal frequencies. Tests with different GPUs show that the energy efficiency, depending on the GPU and the currency, can be increased by up to 84% compared to the usage of the default frequencies. This in turn almost doubles the mining profit.

Further data

Item Type: Article in a journal
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science II > Chair Applied Computer Science II - Univ.-Prof. Dr. Thomas Rauber
Faculties
Faculties > Faculty of Mathematics, Physics und Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science II
Language: English
Originates at UBT: Yes
URN: urn:nbn:de:bvb:703-epub-5098-6
Date Deposited: 23 Sep 2020 09:01
Last Modified: 23 Sep 2020 09:01
URI: https://epub.uni-bayreuth.de/id/eprint/5098

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