Maxim Naumov

According to our database, Maxim Naumov authored at least 42 papers between 2004 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Wukong: Towards a Scaling Law for Large-Scale Recommendation.

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

CoRR, 2024

Disaggregated Multi-Tower: Topology-aware Modeling Technique for Efficient Large Scale Recommendation.

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

Proceedings of the Seventh Annual Conference on Machine Learning and Systems, 2024

Wukong: Towards a Scaling Law for Large-Scale Recommendation.

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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Microscaling Data Formats for Deep Learning.

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

CoRR, 2023

Shared Microexponents: A Little Shifting Goes a Long Way.

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

CoRR, 2023

With Shared Microexponents, A Little Shifting Goes a Long Way.

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

Proceedings of the 50th Annual International Symposium on Computer Architecture, 2023

MTIA: First Generation Silicon Targeting Meta's Recommendation Systems.

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

Proceedings of the 50th Annual International Symposium on Computer Architecture, 2023

2022

Learning to Collide: Recommendation System Model Compression with Learned Hash Functions.

Benjamin Ghaemmaghami, Mustafa Ozdal, Rakesh Komuravelli, Dmitriy Korchev, Dheevatsa Mudigere, Krishnakumar Nair, Maxim Naumov

CoRR, 2022

Software-hardware co-design for fast and scalable training of deep learning recommendation models.

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

Proceedings of the ISCA '22: The 49th Annual International Symposium on Computer Architecture, New York, New York, USA, June 18, 2022

Supporting Massive DLRM Inference through Software Defined Memory.

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

Proceedings of the 42nd IEEE International Conference on Distributed Computing Systems, 2022

2021

Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale.

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

IEEE Micro, 2021

Differentiable NAS Framework and Application to Ads CTR Prediction.

Ravi Krishna, Aravind Kalaiah, Bichen Wu, Maxim Naumov, Dheevatsa Mudigere, Misha Smelyanskiy, Kurt Keutzer

CoRR, 2021

Supporting Massive DLRM Inference Through Software Defined Memory.

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

CoRR, 2021

High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models.

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

CoRR, 2021

Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems.

Antonio A. Ginart, Maxim Naumov, Dheevatsa Mudigere, Jiyan Yang, James Zou

Proceedings of the IEEE International Symposium on Information Theory, 2021

2020

Time-based Sequence Model for Personalization and Recommendation Systems.

Tigran Ishkhanov, Maxim Naumov, Xianjie Chen, Yan Zhu, Yuan Zhong, Alisson Gusatti Azzolini, Chonglin Sun, Frank Jiang, Andrey Malevich, Liang Xiong

CoRR, 2020

Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems.

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

CoRR, 2020

Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems.

Hao-Jun Michael Shi, Dheevatsa Mudigere, Maxim Naumov, Jiyan Yang

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Building Recommender Systems with PyTorch.

Dheevatsa Mudigere, Maxim Naumov, Joe Spisak, Geeta Chauhan, Narine Kokhlikyan, Amanpreet Singh, Vedanuj Goswami

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing.

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

Proceedings of the 47th ACM/IEEE Annual International Symposium on Computer Architecture, 2020

The Architectural Implications of Facebook's DNN-Based Personalized Recommendation.

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

Proceedings of the IEEE International Symposium on High Performance Computer Architecture, 2020

2019

The Architectural Implications of Facebook's DNN-based Personalized Recommendation.

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

CoRR, 2019

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, Misha Smelyanskiy

CoRR, 2019

Spatial-Winograd Pruning Enabling Sparse Winograd Convolution.

Jiecao Yu, Jongsoo Park, Maxim Naumov

CoRR, 2019

On the Dimensionality of Embeddings for Sparse Features and Data.

Maxim Naumov

CoRR, 2019

Bandana: Using Non-Volatile Memory for Storing Deep Learning Models.

Assaf Eisenman, Maxim Naumov, Darryl Gardner, Misha Smelyanskiy, Sergey Pupyrev, Kim M. Hazelwood, Asaf Cidon, Sachin Katti

Proceedings of the Second Conference on Machine Learning and Systems, SysML 2019, 2019

2018

Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications.

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

CoRR, 2018

On Periodic Functions as Regularizers for Quantization of Neural Networks.

Maxim Naumov, Utku Diril, Jongsoo Park, Benjamin Ray, Jedrzej Jablonski, Andrew Tulloch

CoRR, 2018

A Block-Oriented, Parallel and Collective Approach to Sparse Indefinite Preconditioning on GPUs.

Daniel Thuerck, Maxim Naumov, Michael Garland, Michael Goesele

Proceedings of the 8th IEEE/ACM Workshop on Irregular Applications: Architectures and Algorithms, 2018

2017

Parallel Complexity of Forward and Backward Propagation.

Maxim Naumov

CoRR, 2017

AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks.

Aditya Devarakonda, Maxim Naumov, Michael Garland

CoRR, 2017

Feedforward and Recurrent Neural Networks Backward Propagation and Hessian in Matrix Form.

Maxim Naumov

CoRR, 2017

Parallel Depth-First Search for Directed Acyclic Graphs.

Maxim Naumov, Alysson Vrielink, Michael Garland

Proceedings of the Seventh Workshop on Irregular Applications: Architectures and Algorithms, 2017

Parallel jaccard and related graph clustering techniques.

Alexandre Fender, Nahid Emad, Serge G. Petiton, Joe Eaton, Maxim Naumov

Proceedings of the 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, 2017

Parallel Modularity Clustering.

Alexandre Fender, Nahid Emad, Serge G. Petiton, Maxim Naumov

Proceedings of the International Conference on Computational Science, 2017

2015

AmgX: A Library for GPU Accelerated Algebraic Multigrid and Preconditioned Iterative Methods.

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

SIAM J. Sci. Comput., 2015

2011

On the modification of an eigenvalue problem that preserves an eigenspace.

Maxim Naumov

J. Comput. Appl. Math., 2011

2010

A tearing-based hybrid parallel sparse linear system solver.

Maxim Naumov, Murat Manguoglu, Ahmed H. Sameh

J. Comput. Appl. Math., 2010

2009

Multimillion Atom Simulations with Nemo3D.

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

Proceedings of the Encyclopedia of Complexity and Systems Science, 2009

2006

Stability of Semi-implicit Atmospheric Models with Respect to the Reference Temperature Profile.

Andrei Bourchtein, Ludmila Bourchtein, Maxim Naumov

Proceedings of the Numerical Methods and Applications, 6th International Conference, 2006

Semi-Lagrangian Scale Selective Two-Time-Level Scheme for Hydrostatic Atmospheric Model.

Andrei Bourchtein, Ludmila Bourchtein, Maxim Naumov

Proceedings of the Computational Science, 2006

2004

On correctness of the vertical discretization in numerical weather prediction models.

Andrei Bourchtein, Ludmila Bourchtein, Maxim Naumov

Appl. Math. Comput., 2004