Dynamic parameter allocation in parameter servers

Renz-Wieland, Alexander ; Gemulla, Rainer ; Zeuch, Steffen ; Markl, Volker

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DOI: https://doi.org/10.14778/3407790.3407796
URL: https://madoc.bib.uni-mannheim.de/57320
Additional URL: https://dl.acm.org/doi/10.14778/3407790.3407796
URN: urn:nbn:de:bsz:180-madoc-573200
Document Type: Conference or workshop publication
Year of publication: 2020
The title of a journal, publication series: Proceedings of the VLDB Endowment
Volume: 13,12
Page range: 1877-1890
Conference title: 46th International Conference on Very Large Data Bases
Location of the conference venue: Online
Date of the conference: 31.08.-04.09.2020
Place of publication: New York, NY
Publishing house: Association of Computing Machinery
ISSN: 2150-8097
Publication language: English
Institution: School of Business Informatics and Mathematics > Praktische Informatik I (Gemulla 2014-)
License: CC BY 4.0 Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 004 Computer science, internet
Abstract: To keep up with increasing dataset sizes and model complexity, distributed training has become a necessity for large machine learning tasks. Parameter servers ease the implementation of distributed parameter management---a key concern in distributed training---, but can induce severe communication overhead. To reduce communication overhead, distributed machine learning algorithms use techniques to increase parameter access locality (PAL), achieving up to linear speed-ups. We found that existing parameter servers provide only limited support for PAL techniques, however, and therefore prevent efficient training. In this paper, we explore whether and to what extent PAL techniques can be supported, and whether such support is beneficial. We propose to integrate dynamic parameter allocation into parameter servers, describe an efficient implementation of such a parameter server called Lapse, and experimentally compare its performance to existing parameter servers across a number of machine learning tasks. We found that Lapse provides near-linear scaling and can be orders of magnitude faster than existing parameter servers.

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