{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T10:08:29Z","timestamp":1785924509468,"version":"3.56.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,4,2]],"date-time":"2020-04-02T00:00:00Z","timestamp":1585785600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,4,2]],"date-time":"2020-04-02T00:00:00Z","timestamp":1585785600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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.<\/jats:p>","DOI":"10.1007\/s11227-020-03263-5","type":"journal-article","created":{"date-parts":[[2020,4,2]],"date-time":"2020-04-02T22:02:41Z","timestamp":1585864961000},"page":"263-291","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Autotuning based on frequency scaling toward energy efficiency of blockchain algorithms on graphics processing units"],"prefix":"10.1007","volume":"77","author":[{"given":"Matthias","family":"Stachowski","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Fiebig","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Rauber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,4,2]]},"reference":[{"key":"3263_CR1","unstructured":"TOP500.org (2018) TOP500 List November 2018. https:\/\/www.top500.org\/lists\/2018\/11\/. Accessed 18 Mar 2020"},{"key":"3263_CR2","unstructured":"Oak Ridge National Laboratory (2018) SUMMIT. https:\/\/www.olcf.ornl.gov\/olcf-resources\/compute-systems\/summit\/. Accessed 18 Mar 2020"},{"key":"3263_CR3","unstructured":"Konstantopoulos G (2017) Understanding blockchain fundamentals. https:\/\/medium.com\/loom-network\/search?q=Understanding%20Blockchain%20Fundamentals. Accessed 18 Mar 2020"},{"key":"3263_CR4","unstructured":"Bashir I (2017) Mastering blockchain. Packt Publishing Ltd., ISBN: 978-1-78712-544-5"},{"key":"3263_CR5","unstructured":"Unknown (2018) ETHASH. https:\/\/miningbitcoinguide.com\/mining\/sposoby\/ethash. Accessed 18 Mar 2020"},{"key":"3263_CR6","unstructured":"Constantin V (2018) ETHASH. https:\/\/cryptomonday.de\/wie-funktioniert-mining-in-ethereum\/. Accessed 18 Mar 2020"},{"key":"3263_CR7","unstructured":"Cavicchioli M (2018) CryptoNight. https:\/\/monerodocs.org\/proof-of-work\/cryptonight\/. Accessed 18 Mar 2020"},{"key":"3263_CR8","unstructured":"D\u00f6lle M (2018) Ende der Grafikkarten- \u00c4ra: 8000 ASIC-Miner f\u00fcr Zcash, Bitcoin Gold & Co. https:\/\/www.heise.de\/newsticker\/meldung\/Ende-der-Grafikkarten-Aera-8000-ASIC-Miner-fuer-Zcash-Bitcoin-Gold-Co-4091821.html. Accessed 18 Mar 2020"},{"key":"3263_CR9","unstructured":"Vorick D (2018) The state of cryptocurrency mining. https:\/\/blog.sia.tech\/the-state-of-cryptocurrency-mining-538004a37f9b. Accessed 18 Mar 2020"},{"key":"3263_CR10","unstructured":"Biryukov A, Khovratovich D (2018) Equihash: asymmetric proof-of- work based on the generalized birthday problem (full version). https:\/\/orbilu.uni.lu\/bitstream\/10993\/22277\/2\/946.pdf. Accessed 18 Mar 2020"},{"key":"3263_CR11","unstructured":"Cavicchioli M (2018) Proof of work algorithms: Blake2b, Equihash, Tensority and X16R & S. https:\/\/en.cryptonomist.ch\/2019\/07\/28\/mining-algorithms-proof-of-work-2\/. Accessed 18 Mar 2020"},{"key":"3263_CR12","unstructured":"Oberhumer S (2018) ethminer. https:\/\/github.com\/ethereum-mining\/ethminer. Accessed 18 Mar 2020"},{"key":"3263_CR13","unstructured":"fireice-uk. xmr-stak (2018) https:\/\/github.com\/fireice-uk\/xmr-stak. Accessed 18 Mar 2020"},{"key":"3263_CR14","unstructured":"Nicehash (2018) excavator. https:\/\/github.com\/nicehash\/excavator. Accessed 18 Mar 2020"},{"key":"3263_CR15","unstructured":"NVIDIA (2018) NVIDIA Visual Profiler. https:\/\/developer.nvidia.com\/nvidia-visual-profiler. Accessed 18 Mar 2020"},{"key":"3263_CR16","unstructured":"NVIDIA (2019) CUDA C++ programming guide. https:\/\/docs.nvidia.com\/cuda\/cuda-c-programming-guide. Accessed 18 Mar 2020"},{"key":"3263_CR17","unstructured":"NVIDIA (2018) NVIDIA Management Library (NVML). https:\/\/developer.nvidia.com\/nvidia-management-library-nvml. Accessed 18 Mar 2020"},{"key":"3263_CR18","unstructured":"NVIDIA (2018) NVAPI. https:\/\/developer.nvidia.com\/nvapi. Accessed 18 Mar 2020"},{"key":"3263_CR19","unstructured":"NVIDIA (2018) NV-CONTROL X Extension-API specification v 1.6. https:\/\/github.com\/NVIDIA\/nvidia-settings\/blob\/master\/doc\/NV-CONTROL-API.txt. Accessed 18 Mar 2020"},{"key":"3263_CR20","unstructured":"ASL ETHZ (2018) Numerical methods. https:\/\/github.com\/ethz-asl\/numerical_methods. Accessed 18 Mar 2020"},{"key":"3263_CR21","unstructured":"Forum Bitcoin (2017) Ethereum (ETH) mining profit formula. https:\/\/bitcointalk.org\/index.php?topic=2262328.0. Accessed 18 Mar 2020"},{"key":"3263_CR22","unstructured":"CryptoCompare.com (2018) CryptoCompare API. https:\/\/www.cryptocompare.com\/api\/#-api-data-price-. Accessed 18 Mar 2020"},{"key":"3263_CR23","unstructured":"whattomine.com (2018) Coin calculators. https:\/\/whattomine.com\/calculators. Accessed 18 Mar 2020"},{"key":"3263_CR24","unstructured":"Cheng J (2018) Numerical optimization.http:\/\/www.jade-cheng.com\/au\/coalhmm\/optimization\/. Accessed 18 Mar 2020"},{"key":"3263_CR25","unstructured":"Pruvot T (2018) ccminer. https:\/\/github.com\/tpruvot\/ccminer. Accessed 18 Mar 2020"},{"issue":"3","key":"3263_CR26","doi-asserted-by":"publisher","first-page":"1451","DOI":"10.1007\/s11227-014-1236-4","volume":"70","author":"T Rauber","year":"2014","unstructured":"Rauber T et al (2014) Energy measurement, modeling, and prediction for processors with frequency scaling. J Supercomput 70(3):1451\u20131476. https:\/\/doi.org\/10.1007\/s11227-014-1236-4","journal-title":"J Supercomput"},{"key":"3263_CR27","doi-asserted-by":"publisher","first-page":"77","DOI":"10.4236\/ojee.2015.44009","volume":"70","author":"A Mishra","year":"2015","unstructured":"Mishra A, Khare N (2015) Analysis of DVFS techniques for improving the GPU energy efficiency. Open J Energy Effic 4:77\u201386","journal-title":"Open J Energy Effic"},{"key":"3263_CR28","doi-asserted-by":"crossref","unstructured":"Mei X, Yung LS, Zhao K, Chu X (2013) A measurement study of GPU DVFS on energy conservation. https:\/\/www.researchgate.net\/publication\/262365062. Accessed 18 Mar 2020","DOI":"10.1145\/2525526.2525852"},{"key":"3263_CR29","doi-asserted-by":"crossref","unstructured":"Ge R, Vogt R, Majumder J, Alam A, Burtscher M, Zong Z (2013) Effects of dynamic voltage and frequency scaling on a K20 GPU. https:\/\/ieeexplore.ieee.org\/document\/6687422. Accessed 18 Mar 2020","DOI":"10.1109\/ICPP.2013.98"},{"key":"3263_CR30","unstructured":"Cameirinha DM (2015) Exploiting DVFS for GPU energy management. https:\/\/fenix.tecnico.ulisboa.pt\/downloadFile\/563345090414604\/Dissertacao.pdf. Accessed 18 Mar 2020"},{"key":"3263_CR31","doi-asserted-by":"crossref","unstructured":"Dutta B, Adhinarayanan V, Feng W (2018) GPU power prediction via ensemble machine learning for DVFS space exploration. https:\/\/www.researchgate.net\/publication\/326637320. Accessed 18 Mar 2020","DOI":"10.1145\/3203217.3203273"},{"key":"3263_CR32","unstructured":"Park J-G, Dutt N, Lim S-S (2017) ML-Gov: a machine learning enhanced integrated CPU-GPU DVFS governor for mobile gaming. https:\/\/www.researchgate.net\/publication\/320850321. Accessed 18 Mar 2020"},{"key":"3263_CR33","unstructured":"Durillo JJ, Fahringer T (2014) From single- to multi-objective auto-tuning of programs: Advantages and implications. https:\/\/www.researchgate.net\/publication\/271724916. Accessed 18 Mar 2020"},{"key":"3263_CR34","unstructured":"IntelliBreeze Software AB (2018) Awesome Miner.http:\/\/www.awesomeminer.com\/home. Accessed 18 Mar 2020"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-020-03263-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11227-020-03263-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-020-03263-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T23:23:56Z","timestamp":1617319436000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11227-020-03263-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,2]]},"references-count":34,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["3263"],"URL":"https:\/\/doi.org\/10.1007\/s11227-020-03263-5","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,2]]},"assertion":[{"value":"2 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}