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DreamShard: Generalizable Embedding Table Placement for Recommender Systems


Oct 05, 2022
Daochen Zha, Louis Feng, Qiaoyu Tan, Zirui Liu, Kwei-Herng Lai, Bhargav Bhushanam, Yuandong Tian, Arun Kejariwal, Xia Hu

* Accepted by NeurIPS 2022 

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Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems


Sep 02, 2022
Mao Ye, Ruichen Jiang, Haoxiang Wang, Dhruv Choudhary, Xiaocong Du, Bhargav Bhushanam, Aryan Mokhtari, Arun Kejariwal, Qiang Liu


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AutoShard: Automated Embedding Table Sharding for Recommender Systems


Aug 12, 2022
Daochen Zha, Louis Feng, Bhargav Bhushanam, Dhruv Choudhary, Jade Nie, Yuandong Tian, Jay Chae, Yinbin Ma, Arun Kejariwal, Xia Hu

* Accepted by KDD 2022. Code available at https://github.com/daochenzha/autoshard 

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Building a Performance Model for Deep Learning Recommendation Model Training on GPUs


Jan 19, 2022
Zhongyi Lin, Louis Feng, Ehsan K. Ardestani, Jaewon Lee, John Lundell, Changkyu Kim, Arun Kejariwal, John D. Owens

* 11 pages, 10 figures 

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Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems


May 04, 2021
Xiaocong Du, Bhargav Bhushanam, Jiecao Yu, Dhruv Choudhary, Tianxiang Gao, Sherman Wong, Louis Feng, Jongsoo Park, Yu Cao, Arun Kejariwal


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Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data


Oct 21, 2020
Mao Ye, Dhruv Choudhary, Jiecao Yu, Ellie Wen, Zeliang Chen, Jiyan Yang, Jongsoo Park, Qiang Liu, Arun Kejariwal


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Fast Distributed Training of Deep Neural Networks: Dynamic Communication Thresholding for Model and Data Parallelism


Oct 18, 2020
Vipul Gupta, Dhruv Choudhary, Ping Tak Peter Tang, Xiaohan Wei, Xing Wang, Yuzhen Huang, Arun Kejariwal, Kannan Ramchandran, Michael W. Mahoney

* 17 pages, 8 figures 

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On the Runtime-Efficacy Trade-off of Anomaly Detection Techniques for Real-Time Streaming Data


Oct 12, 2017
Dhruv Choudhary, Arun Kejariwal, Francois Orsini

* 12 pages 

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Real Time Analytics: Algorithms and Systems


Aug 07, 2017
Arun Kejariwal, Sanjeev Kulkarni, Karthik Ramasamy

* Extended version of VLDB'15 tutorial proposal 

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