ElDA: LDA made efficient via algorithm-system codesign submission

Shilong Wang, Da Li, Hengyong Yu, Hang Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Latent Dirichlet Allocation (LDA) is a statistical approach for topic modeling with a wide range of applications. In spite of the significance, we observe very few attempts from system track to improve LDA, let alone the algorithm and system codesigned efforts. To this end, we propose eLDA with an algorithm-system codesigned optimization. Particularly, we introduce a novel three-branch sampling mechanism to taking advantage of the convergence heterogeneity of various tokens in order to reduce redundant sampling task. Our evaluation shows that eLDA outperforms the state-of-the-arts.

Original languageEnglish (US)
Title of host publicationPPoPP 2020 - Proceedings of the 2020 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
PublisherAssociation for Computing Machinery
Pages407-408
Number of pages2
ISBN (Electronic)9781450368186
DOIs
StatePublished - Feb 19 2020
Externally publishedYes
Event25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2020 - San Diego, United States
Duration: Feb 22 2020Feb 26 2020

Publication series

NameProceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP

Conference

Conference25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2020
Country/TerritoryUnited States
CitySan Diego
Period2/22/202/26/20

All Science Journal Classification (ASJC) codes

  • Software

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