NuPS: A parameter server for machine learning with non-uniform parameter access

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DOI:	https://doi.org/10.1145/3514221.3517860
URL:	https://dl.acm.org/doi/10.1145/3514221.3517860
URN:	urn:nbn:de:bsz:180-madoc-647738
Document Type:	Conference or workshop publication
Year of publication:	2022
Book title:	SIGMOD '22 : proceedings of the 2022 International Conference on Management of Data : June 12-17, 2022, Philadelphia, PA, USA
Page range:	481-495
Conference title:	SIGMOD '22
Location of the conference venue:	Philadelphia, PA
Date of the conference:	12.-17.06.2022
Publisher:	Ives, Zachary ; Bonifati, Angela ; El Abbadi, Amr
Place of publication:	New York, NY
Publishing house:	ACM
ISBN:	978-1-4503-9249-5
Publication language:	English
Institution:	School of Business Informatics and Mathematics > Practical Computer Science I: Data Analytics (Gemulla 2014-)
Pre-existing license:	Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject:	004 Computer science, internet
Keywords (English):	parameter servers , distributed machine learning , large-scale machine learning , skew , sampling

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BASE: Renz-Wieland, Alexander ; Gemulla, Rainer ; Kaoudi, Zoi ; Markl, Volker

Google Scholar: Renz-Wieland, Alexander ; Gemulla, Rainer ; Kaoudi, Zoi ; Markl, Volker

ORCID: Renz-Wieland, Alexander, Gemulla, Rainer

, Kaoudi, Zoi and Markl, Volker

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