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Recommendation models rely on deep learning networks and large embedding tables, resulting in computationally and memory-intensive processes.
How to construct pseudorandom permutations from pseudorandom functions
Michael Luby and Charles Rackoff · 1988
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WAVE: Popularity-based and collaborative in-network caching for content-oriented networks
K. Cho, M. Lee, K. Park, T. T. Kwon, Y. Choi, and Sangheon Pack · 2012
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Popularity versus similarity in growing networks
Fragkiskos Papadopoulos, Maksim Kitsak, M. A. Serrano, Marian Boguna, and Dmitri Krioukov · 2012
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Terabyte Click Logs, b
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Compressing neural networks with the hashing trick
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Neural collaborative filtering
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua · 2017
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Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Accelerating wide deep recommender inference on gpus, 2017
Nvidia · 2017
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Scale-Out Acceleration for Machine Learnng
Jongse Park, Hardik Sharma, Divya Mahajan, Joon Kyung Kim, Preston Olds, and Hadi Esmaeilzadeh · 2017
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In-Datacenter Performance Analysis of a Tensor Processing Unit
Norman P. Jouppi, Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, Suresh Bhatia, Nan Boden, Al Borchers, Rick Boyle, Pierre-luc Cantin, Clifford Chao, Chris Clark, Jeremy Coriell, Mike Daley, Matt Dau, Jeffrey Dean, Ben Gelb, Tara Vazir Ghaemmaghami, Rajendra Gottipati, William Gulland, Robert Hagmann, C. Richard Ho, Doug Hogberg, John Hu, Robert Hundt, Dan Hurt, Julian Ibarz, Aaron Jaffey, Alek Jaworski, Alexander Kaplan, Harshit Khaitan, Daniel Killebrew, Andy Koch, Naveen Kumar, Steve Lacy, James Laudon, James Law, Diemthu Le, Chris Leary, Zhuyuan Liu, Kyle Lucke, Alan Lundin, Gordon MacKean, Adriana Maggiore, Maire Mahony, Kieran Miller, Rahul Nagarajan, Ravi Narayanaswami, Ray Ni, Kathy Nix, Thomas Norrie, Mark Omernick, Narayana Penukonda, Andy Phelps, Jonathan Ross, Matt Ross, Amir Salek, Emad Samadiani, Chris Severn, Gregory Sizikov, Matthew Snelham, Jed Souter, Dan Steinberg, Andy Swing, Mercedes Tan, Gregory Thorson, Bo Tian, Horia Toma, Erick Tuttle, Vijay Vasudevan, Richard Walter, Walter Wang, Eric Wilcox, and Doe Hyun Yoon · 2017
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Gist: Efficient Data Encoding for Deep Neural Network Training
A. Jain, A. Phanishayee, J. Mars, L. Tang, and G. Pekhimenko · 2018
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Serving DNNs in Real Time at Datacenter Scale with Project Brainwave
Eric Chung, Jeremy Fowers, Kalin Ovtcharov, , Adrian Caulfield, Todd Massengill, Ming Liu, Mahdi Ghandi, Daniel Lo, Steve Reinhardt, Shlomi Alkalay, Hari Angepat, Derek Chiou, Alessandro Forin, Doug Burger, Lisa Woods, Gabriel Weisz, Michael Haselman, and Dan Zhang · 2018
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In-rdbms hardware acceleration of advanced analytics
Divya Mahajan, Joon Kyung Kim, Jacob Sacks, Adel Ardalan, Arun Kumar, and Hadi Esmaeilzadeh · 2018
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Deep Learning Recommendation Model for Personalization and Recommendation Systems
Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson G. Azzolini, Dmytro Dzhulgakov, Andrey Mallevich, Ilia Cherniavskii, Yinghai Lu, Raghuraman Krishnamoorthi, Ansha Yu, Volodymyr Kondratenko, Stephanie Pereira, Xianjie Chen, Wenlin Chen, Vijay Rao, Bill Jia, Liang Xiong, and Misha Smelyanskiy · 2019
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Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads
Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, unjie Qian, Wencong Xiao, and Fan Yang · 2019
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XDL: An Industrial Deep Learning Framework for High-Dimensional Sparse Data
Biye Jiang, Chao Deng, Huimin Yi, Zelin Hu, Guorui Zhou, Yang Zheng, Sui Huang, Xinyang Guo, Dongyue Wang, Yue Song, Liqin Zhao, Zhi Wang, Peng Sun, Yu Zhang, Di Zhang, Jinhui Li, Jian Xu, Xiaoqiang Zhu, and Kun Gai · 2019
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Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems
A. Ginart, M. Naumov, D. Mudigere, Jiyan Yang, and J. Zou · 2019
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Mixed-Precision Embedding Using a Cache, 2020
Jie Amy Yang, Jianyu Huang, Jongsoo Park, Ping Tak Peter Tang, and Andrew Tulloch · 2020
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A Generic Network Compression Framework for Sequential Recommender Systems
Yang Sun, Fajie Yuan, Min Yang, Guoao Wei, Zhou Zhao, and Duo Liu · 2020
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Meta recommender model training on ZionEX devices
meta · 2021
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Cdlrm: Look ahead caching for scalable training of recommendation models
Keshav Balasubramanian, Abdulla Alshabanah, Joshua D Choe, and Murali Annavaram · 2021
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Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models, 2021
Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang, Zhihao Jia, Andrew Tulloch, Srinivas Sridharan, Xing Liu, Mustafa Ozdal, Jade Nie, Jongsoo Park, Liang Luo, Jie Amy Yang, Leon Gao, Dmytro Ivchenko, Aarti Basant, Yuxi Hu, Jiyan Yang, Ehsan K. Ardestani, Xiaodong Wang, Rakesh Komuravelli, Ching-Hsiang Chu, Serhat Yilmaz, Huayu Li, Jiyuan Qian, Zhuobo Feng, Yinbin Ma, Junjie Yang, Ellie Wen, Hong Li, Lin Yang, Chonglin Sun, Whitney Zhao, Dimitry Melts, Krishna Dhulipala, KR Kishore, Tyler Graf, Assaf Eisenman, Kiran Kumar Matam, Adi Gangidi, Guoqiang Jerry Chen, Manoj Krishnan, Avinash Nayak, Krishnakumar Nair, Bharath Muthiah, Mahmoud khorashadi, Pallab Bhattacharya, Petr Lapukhov, Maxim Naumov, Ajit Mathews, Lin Qiao, Mikhail Smelyanskiy, Bill Jia, and Vijay Rao · 2021
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Bandana: Using non-volatile memory for storing deep learning models
Assaf Eisenman, Maxim Naumov, Darryl Gardner, Misha Smelyanskiy, Sergey Pupyrev, Kim Hazelwood, Asaf Cidon, and Sachin Katti · 2019
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2019
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Understanding Training Efficiency of Deep Learning Recommendation Models at Scale, 2020
Bilge Acun, Matthew Murphy, Xiaodong Wang, Jade Nie, Carole-Jean Wu, and Kim Hazelwood · 2020
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Embedding-Based Retrieval in Facebook Search , page 2553–2561
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Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems, 2020
Weijie Zhao, Deping Xie, Ronglai Jia, Yulei Qian, Ruiquan Ding, Mingming Sun, and Ping Li · 2020
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Time-based Sequence Model for Personalization and Recommendation Systems
T. Ishkhanov, M. Naumov, X. Chen, Y. Zhu, Y. Zhong, A. G. Azzolini, C. Sun, F. Jiang, A. Malevich, and L. Xiong · 2020
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Optimizing Deep Learning Recommender Systems Training on CPU Cluster Architectures
Dhiraj Kalamkar, Evangelos Georganas, Sudarshan Srinivasan, Jianping Chen, Mikhail Shiryaev, and Alexander Heinecke · 2020
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Learning to embed categorical features without embedding tables for recommendation
Wang-Cheng Kang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Ting Chen, Lichan Hong, and Ed H Chi · 2021
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Understanding data storage and ingestion for large-scale deep recommendation model training: Industrial product
Mark Zhao, Niket Agarwal, Aarti Basant, Buğra Gedik, Satadru Pan, Mustafa Ozdal, Rakesh Komuravelli, Jerry Pan, Tianshu Bao, Haowei Lu, Sundaram Narayanan, Jack Langman, Kevin Wilfong, Harsha Rastogi, Carole-Jean Wu, Christos Kozyrakis, and Parik Pol · 2022
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Accelerating Recommendation System Trainingby Leveraging Popular Choices
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Recshard: Statistical feature-based memory optimization for industry-scale neural recommendation, 2022
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Bagpipe: Accelerating deep recommendation model training, 2022
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El-rec: efficient large-scale recommendation model training via tensor-train embedding table
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Random offset block embedding (robe) for compressed embedding tables in deep learning recommendation systems
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Grace: A scalable graph-based approach to accelerating recommendation model inference
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Evstore: Storage and caching capabilities for scaling embedding tables in deep recommendation systems
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Mp-rec: Hardware-software co-design to enable multi-path recommendation
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