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ORCIDMaxim Naumov
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2020 – today
- 2024
[c18]Buyun Zhang, Liang Luo, Yuxin Chen, Jade Nie, Xi Liu, Shen Li, Yanli Zhao, Yuchen Hao, Yantao Yao, Ellie Dingqiao Wen, Jongsoo Park, Maxim Naumov, Wenlin Chen:
Wukong: Towards a Scaling Law for Large-Scale Recommendation. ICML 2024- [c17]Liang Luo, Buyun Zhang, Michael Tsang, Yinbin Ma, Ching-Hsiang Chu, Yuxin Chen, Shen Li, Yuchen Hao, Yanli Zhao, Guna Lakshminarayanan, Ellie Wen, Jongsoo Park, Dheevatsa Mudigere, Maxim Naumov:
Disaggregated Multi-Tower: Topology-aware Modeling Technique for Efficient Large Scale Recommendation. MLSys 2024 - [i24]Liang Luo, Buyun Zhang, Michael Tsang, Yinbin Ma, Ching-Hsiang Chu, Yuxin Chen, Shen Li, Yuchen Hao, Yanli Zhao, Guna Lakshminarayanan, Ellie Dingqiao Wen, Jongsoo Park, Dheevatsa Mudigere, Maxim Naumov:
Disaggregated Multi-Tower: Topology-aware Modeling Technique for Efficient Large-Scale Recommendation. CoRR abs/2403.00877 (2024) - [i23]Buyun Zhang, Liang Luo, Yuxin Chen, Jade Nie, Xi Liu, Daifeng Guo, Yanli Zhao, Shen Li, Yuchen Hao, Yantao Yao, Guna Lakshminarayanan, Ellie Dingqiao Wen, Jongsoo Park, Maxim Naumov, Wenlin Chen:
Wukong: Towards a Scaling Law for Large-Scale Recommendation. CoRR abs/2403.02545 (2024) - 2023
- [c16]Amin Firoozshahian
, Joel Coburn, Roman Levenstein, Rakesh Nattoji, Ashwin Kamath, Olívia Wu, Gurdeepak Grewal, Harish Aepala, Bhasker Jakka, Bob Dreyer, Adam Hutchin, Utku Diril, Krishnakumar Nair, Ehsan K. Ardestani, Martin Schatz, Yuchen Hao, Rakesh Komuravelli, Kunming Ho, Sameer Abu Asal, Joe Shajrawi, Kevin Quinn, Nagesh Sreedhara, Pankaj Kansal, Willie Wei, Dheepak Jayaraman, Linda Cheng, Pritam Chopda, Eric Wang, Ajay Bikumandla, Arun Karthik Sengottuvel, Krishna Thottempudi, Ashwin Narasimha, Brian Dodds, Cao Gao, Jiyuan Zhang, Mohammed Al-Sanabani, Ana Zehtabioskuie, Jordan Fix, Hangchen Yu, Richard Li, Kaustubh Gondkar, Jack Montgomery, Mike Tsai, Saritha Dwarakapuram, Sanjay Desai, Nili Avidan, Poorvaja Ramani, Karthik Narayanan, Ajit Mathews, Sethu Gopal, Maxim Naumov, Vijay Rao, Krishna Noru, Harikrishna Reddy, Prahlad Venkatapuram, Alexis Bjorlin:
MTIA: First Generation Silicon Targeting Meta's Recommendation Systems. ISCA 2023: 80:1-80:13 - [c15]Bita Darvish Rouhani, Ritchie Zhao, Venmugil Elango, Rasoul Shafipour, Mathew Hall, Maral Mesmakhosroshahi, Ankit More, Levi Melnick, Maximilian Golub, Girish Varatkar, Lai Shao, Gaurav Kolhe, Dimitry Melts, Jasmine Klar, Renee L'Heureux, Matt Perry, Doug Burger, Eric S. Chung, Zhaoxia (Summer) Deng, Sam Naghshineh, Jongsoo Park, Maxim Naumov:
With Shared Microexponents, A Little Shifting Goes a Long Way. ISCA 2023: 83:1-83:13 - [i22]Bita Rouhani, Ritchie Zhao, Venmugil Elango, Rasoul Shafipour, Mathew Hall, Maral Mesmakhosroshahi, Ankit More, Levi Melnick, Maximilian Golub, Girish Varatkar, Lei Shao, Gaurav Kolhe, Dimitry Melts, Jasmine Klar, Renee L'Heureux, Matt Perry, Doug Burger, Eric S. Chung, Zhaoxia Deng, Sam Naghshineh, Jongsoo Park, Maxim Naumov:
Shared Microexponents: A Little Shifting Goes a Long Way. CoRR abs/2302.08007 (2023) - [i21]Bita Darvish Rouhani, Ritchie Zhao, Ankit More, Mathew Hall, Alireza Khodamoradi, Summer Deng, Dhruv Choudhary, Marius Cornea, Eric Dellinger, Kristof Denolf, Dusan Stosic, Venmugil Elango, Maximilian Golub, Alexander Heinecke, Phil James-Roxby, Dharmesh Jani, Gaurav Kolhe, Martin Langhammer, Ada Li, Levi Melnick, Maral Mesmakhosroshahi, Andres Rodriguez, Michael Schulte, Rasoul Shafipour, Lei Shao, Michael Y. Siu, Pradeep Dubey, Paulius Micikevicius, Maxim Naumov, Colin Verilli, Ralph Wittig, Doug Burger, Eric S. Chung:
Microscaling Data Formats for Deep Learning. CoRR abs/2310.10537 (2023) - 2022
- [c14]Ehsan K. Ardestani, Changkyu Kim, Seung Jae Lee, Luoshang Pan, Jens Axboe, Valmiki Rampersad, Banit Agrawal, Fuxun Yu, Ansha Yu, Trung Le, Hector Yuen, Dheevatsa Mudigere, Shishir Juluri, Akshat Nanda, Manoj Wodekar, Krishnakumar Nair, Maxim Naumov, Chris Petersen, Mikhail Smelyanskiy, Vijay Rao:
Supporting Massive DLRM Inference through Software Defined Memory. ICDCS 2022: 302-312 - [c13]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, K. R. 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, Vijay Rao:
Software-hardware co-design for fast and scalable training of deep learning recommendation models. ISCA 2022: 993-1011 - [i20]Benjamin Ghaemmaghami, Mustafa Ozdal, Rakesh Komuravelli, Dmitriy Korchev, Dheevatsa Mudigere, Krishnakumar Nair, Maxim Naumov:
Learning to Collide: Recommendation System Model Compression with Learned Hash Functions. CoRR abs/2203.15837 (2022) - 2021
- [j5]Zhaoxia Deng, Jongsoo Park, Ping Tak Peter Tang, Haixin Liu, Jie Yang, Hector Yuen, Jianyu Huang, Daya Shanker Khudia, Xiaohan Wei, Ellie Wen, Dhruv Choudhary, Raghuraman Krishnamoorthi, Carole-Jean Wu, Nadathur Satish, Changkyu Kim, Maxim Naumov, Sam Naghshineh, Mikhail Smelyanskiy:
Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale. IEEE Micro 41(5): 93-100 (2021) - [c12]Antonio A. Ginart, Maxim Naumov, Dheevatsa Mudigere, Jiyan Yang, James Zou:
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems. ISIT 2021: 2786-2791 - [i19]Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang, 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, K. R. 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, Lin Qiao, Mikhail Smelyanskiy, Bill Jia, Vijay Rao:
High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models. CoRR abs/2104.05158 (2021) - [i18]Zhaoxia Deng, Jongsoo Park, Ping Tak Peter Tang, Haixin Liu, Jie Yang, Hector Yuen, Jianyu Huang, Daya Shanker Khudia, Xiaohan Wei, Ellie Wen, Dhruv Choudhary, Raghuraman Krishnamoorthi, Carole-Jean Wu, Nadathur Satish, Changkyu Kim, Maxim Naumov, Sam Naghshineh, Mikhail Smelyanskiy:
Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale. CoRR abs/2105.12676 (2021) - [i17]Ehsan K. Ardestani, Changkyu Kim, Seung Jae Lee, Luoshang Pan, Valmiki Rampersad, Jens Axboe, Banit Agrawal, Fuxun Yu, Ansha Yu, Trung Le, Hector Yuen, Shishir Juluri, Akshat Nanda, Manoj Wodekar, Dheevatsa Mudigere, Krishnakumar Nair, Maxim Naumov, Chris Peterson, Mikhail Smelyanskiy, Vijay Rao:
Supporting Massive DLRM Inference Through Software Defined Memory. CoRR abs/2110.11489 (2021) - [i16]Ravi Krishna, Aravind Kalaiah, Bichen Wu, Maxim Naumov, Dheevatsa Mudigere, Misha Smelyanskiy, Kurt Keutzer:
Differentiable NAS Framework and Application to Ads CTR Prediction. CoRR abs/2110.14812 (2021) - 2020
- [c11]Udit Gupta, Carole-Jean Wu, Xiaodong Wang, Maxim Naumov, Brandon Reagen, David Brooks, Bradford Cottel, Kim M. Hazelwood, Mark Hempstead, Bill Jia, Hsien-Hsin S. Lee, Andrey Malevich, Dheevatsa Mudigere, Mikhail Smelyanskiy, Liang Xiong, Xuan Zhang:
The Architectural Implications of Facebook's DNN-Based Personalized Recommendation. HPCA 2020: 488-501 - [c10]Liu Ke, Udit Gupta, Benjamin Youngjae Cho, David Brooks, Vikas Chandra, Utku Diril, Amin Firoozshahian, Kim M. Hazelwood, Bill Jia, Hsien-Hsin S. Lee, Meng Li, Bert Maher, Dheevatsa Mudigere, Maxim Naumov, Martin Schatz, Mikhail Smelyanskiy, Xiaodong Wang, Brandon Reagen, Carole-Jean Wu, Mark Hempstead, Xuan Zhang:
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing. ISCA 2020: 790-803 - [c9]Hao-Jun Michael Shi, Dheevatsa Mudigere, Maxim Naumov, Jiyan Yang:
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems. KDD 2020: 165-175 - [c8]Dheevatsa Mudigere, Maxim Naumov, Joe Spisak, Geeta Chauhan, Narine Kokhlikyan, Amanpreet Singh, Vedanuj Goswami:
Building Recommender Systems with PyTorch. KDD 2020: 3525-3526 - [i15]Maxim Naumov, John Kim, Dheevatsa Mudigere, Srinivas Sridharan, Xiaodong Wang, Whitney Zhao, Serhat Yilmaz, Changkyu Kim, Hector Yuen, Mustafa Ozdal, Krishnakumar Nair, Isabel Gao, Bor-Yiing Su, Jiyan Yang, Mikhail Smelyanskiy:
Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems. CoRR abs/2003.09518 (2020) - [i14]Tigran Ishkhanov, Maxim Naumov, Xianjie Chen, Yan Zhu, Yuan Zhong, Alisson Gusatti Azzolini, Chonglin Sun, Frank Jiang, Andrey Malevich, Liang Xiong:
Time-based Sequence Model for Personalization and Recommendation Systems. CoRR abs/2008.11922 (2020)
2010 – 2019
- 2019
- [c7]Assaf Eisenman, Maxim Naumov, Darryl Gardner, Misha Smelyanskiy, Sergey Pupyrev, Kim M. Hazelwood, Asaf Cidon, Sachin Katti:
Bandana: Using Non-Volatile Memory for Storing Deep Learning Models. SysML 2019 - [i13]Maxim Naumov:
On the Dimensionality of Embeddings for Sparse Features and Data. CoRR abs/1901.02103 (2019) - [i12]Jiecao Yu, Jongsoo Park, Maxim Naumov:
Spatial-Winograd Pruning Enabling Sparse Winograd Convolution. CoRR abs/1901.02132 (2019) - [i11]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, Misha Smelyanskiy:
Deep Learning Recommendation Model for Personalization and Recommendation Systems. CoRR abs/1906.00091 (2019) - [i10]Udit Gupta, Xiaodong Wang, Maxim Naumov, Carole-Jean Wu, Brandon Reagen, David Brooks, Bradford Cottel, Kim M. Hazelwood, Bill Jia, Hsien-Hsin S. Lee, Andrey Malevich, Dheevatsa Mudigere, Mikhail Smelyanskiy, Liang Xiong, Xuan Zhang:
The Architectural Implications of Facebook's DNN-based Personalized Recommendation. CoRR abs/1906.03109 (2019) - [i9]Hao-Jun Michael Shi, Dheevatsa Mudigere, Maxim Naumov, Jiyan Yang:
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems. CoRR abs/1909.02107 (2019) - [i8]Antonio Ginart, Maxim Naumov, Dheevatsa Mudigere, Jiyan Yang, James Zou:
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems. CoRR abs/1909.11810 (2019) - [i7]Liu Ke, Udit Gupta, Carole-Jean Wu, Benjamin Youngjae Cho, Mark Hempstead, Brandon Reagen, Xuan Zhang, David M. Brooks, Vikas Chandra, Utku Diril, Amin Firoozshahian, Kim M. Hazelwood, Bill Jia, Hsien-Hsin S. Lee, Meng Li, Bert Maher, Dheevatsa Mudigere, Maxim Naumov, Martin Schatz, Mikhail Smelyanskiy, Xiaodong Wang:
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing. CoRR abs/1912.12953 (2019) - 2018
- [c6]Daniel Thuerck, Maxim Naumov, Michael Garland, Michael Goesele:
A Block-Oriented, Parallel and Collective Approach to Sparse Indefinite Preconditioning on GPUs. IA3@SC 2018: 1-10 - [i6]Assaf Eisenman, Maxim Naumov, Darryl Gardner, Misha Smelyanskiy, Sergey Pupyrev, Kim M. Hazelwood, Asaf Cidon, Sachin Katti:
Bandana: Using Non-volatile Memory for Storing Deep Learning Models. CoRR abs/1811.05922 (2018) - [i5]Maxim Naumov, Utku Diril, Jongsoo Park, Benjamin Ray, Jedrzej Jablonski, Andrew Tulloch:
On Periodic Functions as Regularizers for Quantization of Neural Networks. CoRR abs/1811.09862 (2018) - [i4]Jongsoo Park, Maxim Naumov, Protonu Basu, Summer Deng, Aravind Kalaiah, Daya Shanker Khudia, James Law, Parth Malani, Andrey Malevich, Nadathur Satish, Juan Miguel Pino, Martin Schatz, Alexander Sidorov, Viswanath Sivakumar, Andrew Tulloch, Xiaodong Wang, Yiming Wu, Hector Yuen, Utku Diril, Dmytro Dzhulgakov, Kim M. Hazelwood, Bill Jia, Yangqing Jia, Lin Qiao, Vijay Rao, Nadav Rotem, Sungjoo Yoo, Mikhail Smelyanskiy:
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications. CoRR abs/1811.09886 (2018) - 2017
- [c5]Alexandre Fender, Nahid Emad, Serge G. Petiton, Maxim Naumov:
Parallel Modularity Clustering. ICCS 2017: 1793-1802 - [c4]Alexandre Fender, Nahid Emad, Serge G. Petiton, Joe Eaton, Maxim Naumov:
Parallel jaccard and related graph clustering techniques. ScalA@SC 2017: 4:1-4:8 - [c3]Maxim Naumov, Alysson Vrielink, Michael Garland:
Parallel Depth-First Search for Directed Acyclic Graphs. IA3@SC 2017: 4:1-4:8 - [i3]Maxim Naumov:
Feedforward and Recurrent Neural Networks Backward Propagation and Hessian in Matrix Form. CoRR abs/1709.06080 (2017) - [i2]Aditya Devarakonda, Maxim Naumov, Michael Garland:
AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks. CoRR abs/1712.02029 (2017) - [i1]Maxim Naumov:
Parallel Complexity of Forward and Backward Propagation. CoRR abs/1712.06577 (2017) - 2015
- [j4]Maxim Naumov, M. Arsaev, Patrice Castonguay, Jonathan M. Cohen, Julien Demouth, Joe Eaton, Simon K. Layton, N. Markovskiy, István Z. Reguly, Nikolai Sakharnykh, V. Sellappan, Robert Strzodka:
AmgX: A Library for GPU Accelerated Algebraic Multigrid and Preconditioned Iterative Methods. SIAM J. Sci. Comput. 37(5) (2015) - 2011
- [j3]Maxim Naumov:
On the modification of an eigenvalue problem that preserves an eigenspace. J. Comput. Appl. Math. 235(18): 5432-5440 (2011) - 2010
- [j2]Maxim Naumov, Murat Manguoglu, Ahmed H. Sameh:
A tearing-based hybrid parallel sparse linear system solver. J. Comput. Appl. Math. 234(10): 3025-3038 (2010)
2000 – 2009
- 2009
- [r1]Shaikh S. Ahmed, Neerav Kharche, Rajib Rahman, Muhammad Usman, Sunhee Lee, Hoon Ryu, Hansang Bae, Steven M. Clark, Benjamin Haley, Maxim Naumov, Faisal Saied, Marek Korkusinski, Rick Kennell, Michael McLennan, Timothy B. Boykin, Gerhard Klimeck:
Multimillion Atom Simulations with Nemo3D. Encyclopedia of Complexity and Systems Science 2009: 5745-5783 - 2006
- [c2]Andrei Bourchtein, Ludmila Bourchtein, Maxim Naumov:
Semi-Lagrangian Scale Selective Two-Time-Level Scheme for Hydrostatic Atmospheric Model. International Conference on Computational Science (1) 2006: 258-266 - [c1]Andrei Bourchtein, Ludmila Bourchtein, Maxim Naumov:
Stability of Semi-implicit Atmospheric Models with Respect to the Reference Temperature Profile. Numerical Methods and Applications 2006: 427-434 - 2004
- [j1]Andrei Bourchtein, Ludmila Bourchtein, Maxim Naumov:
On correctness of the vertical discretization in numerical weather prediction models. Appl. Math. Comput. 158(2): 513-527 (2004)
Coauthor Index
Mikhail Smelyanskiy
aka: Misha Smelyanskiy
aka: Misha Smelyanskiy
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