Bagpipe: Accelerating Deep Recommendation Model Training

Author:

Agarwal Saurabh1ORCID,Yan Chengpo2ORCID,Zhang Ziyi3ORCID,Venkataraman Shivaram1ORCID

Affiliation:

1. Department of Computer Science, University of Wisconsin-Madison, Madison, WI, United States of America

2. Department of Computer Science, University of Wisconsin-Madison, Madison, WI, USA

3. Department of Computer Science, University of Chicago, Chicago, IL, USA

Publisher

ACM

Reference68 articles.

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2. Understanding Training Efficiency of Deep Learning Recommendation Models at Scale

3. Lada A Adamic and Bernardo A Huberman . Power-law distribution of the world wide web. science, 287(5461):2115--2115 , 2000 . Lada A Adamic and Bernardo A Huberman. Power-law distribution of the world wide web. science, 287(5461):2115--2115, 2000.

4. Accelerating recommendation system training by leveraging popular choices

5. Michael Armbrust , Ali Ghodsi , Reynold Xin , and Matei Zaharia . Lake-house : a new generation of open platforms that unify data warehousing and advanced analytics . In Proceedings of CIDR , 2021 . Michael Armbrust, Ali Ghodsi, Reynold Xin, and Matei Zaharia. Lake-house: a new generation of open platforms that unify data warehousing and advanced analytics. In Proceedings of CIDR, 2021.

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3. NDRec: A Near-Data Processing System for Training Large-Scale Recommendation Models;IEEE Transactions on Computers;2024-05

4. UGACHE: A Unified GPU Cache for Embedding-based Deep Learning;Proceedings of the 29th Symposium on Operating Systems Principles;2023-10-23

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