Maxim Naumov

54 papers A* 10A 1B 1C 1Misc 2Journal 31Unranked 7
YearRankTypeTitle / Venue / Authors
2026 — conf
Meta Lattice: Model Space Redesign for Cost-Effective Industry-Scale Ads Recommendations.
KDD (1)
Liang Luo, Yuxin Chen, Zhengyu Zhang, Mengyue Hang, Andrew Gu, Buyun Zhang, Boyang Liu, Chen Chen, Fan Yang, Feifan Gu, Huayu Li, Jade Nie, Jiayi Xu, Jiyan Yang, Jongsoo Park, Laming Chen, Longhao Jin, Qin Huang, Shali Jiang, Shiwen Shen, Shuaiwen Wang, Siyang Yuan, Tongyi Tang, Weilin Zhang, Xi Liu, Xiaohan Wei, Yuchen Hao, Xiaozhen Xia, Yasmine Badr, ZeLiang Chen, Chengze Fan, Dong Liang, Qianru Li, Sihan Zeng, Wenjun Wang, Yunlong He, Yinbin Ma, Maxim Naumov, Yantao Yao, Wenlin Chen, Ellie Dingqiao Wen
2026 J jnl
Training LLMs with Fault Tolerant HSDP on 100,000 GPUs.
CoRR
Omkar Salpekar, Rohan Varma, Kenny Yu, Vladimir Ivanov, Yang Wang, Ahmed Sharif, Min Si, Shawn Xu, Feng Tian, Shengbao Zheng, Tristan Rice, Ankush Garg, Shangfu Peng, Shreyas Siravara, Wenyin Fu, Rodrigo de Castro, Adithya Gangidi, Andrey Obraztsov, Sharan Narang, Sergey Edunov, Maxim Naumov, Chunqiang Tang, Mathew Oldham
2025 C conf
An Empirical Study of Microscaling Formats for Low-Precision LLM Training.
ARITH
Hanmei Yang, Summer Deng, Amit Nagpal, Maxim Naumov, Mohammad Janani, Tongping Liu, Hui Guan
2025 A* conf
DCPerf: An Open-Source, Battle-Tested Performance Benchmark Suite for Datacenter Workloads.
ISCA
Wei Su, Abhishek Dhanotia, Carlos Torres, Jayneel Gandhi, Neha Gholkar, Shobhit O. Kanaujia, Maxim Naumov, Kalyan Subramanian, Valentin Andrei, Yifan Yuan, Chunqiang Tang
2025 J jnl
Meta Lattice: Model Space Redesign for Cost-Effective Industry-Scale Ads Recommendations.
CoRR
Liang Luo, Yuxin Chen, Zhengyu Zhang, Mengyue Hang, Andrew Gu, Buyun Zhang, Boyang Liu, Chen Chen, Chengze Fan, Dong Liang, Fan Yang, Feifan Gu, Huayu Li, Jade Nie, Jiayi Xu, Jiyan Yang, Jongsoo Park, Laming Chen, Longhao Jin, Qianru Li, Qin Huang, Shali Jiang, Shiwen Shen, Shuaiwen Wang, Sihan Zeng, Siyang Yuan, Tongyi Tang, Weilin Zhang, Wenjun Wang, Xi Liu, Xiaohan Wei, Xiaozhen Xia, Yuchen Hao, Yunlong He, Yasmine Badr, ZeLiang Chen, Maxim Naumov, Yantao Yao, Wenlin Chen, Santanu Kolay, GP Musumeci, Ellie Dingqiao Wen
2025 A* conf
Meta's Second Generation AI Chip: Model-Chip Co-Design and Productionization Experiences.
ISCA
Joel Coburn, Chunqiang Tang, Sameer Abu Asal, Neeraj Agrawal, Raviteja Chinta, Harish Dattatraya Dixit, Brian Dodds, Saritha Dwarakapuram, Amin Firoozshahian, Cao Gao, Kaustubh Gondkar, Tyler Graf, Junhan Hu, Jian Huang, Sterling Hughes, Adam Hutchin, Bhasker Jakka, Guoqiang Jerry Chen, Indu Kalyanaraman, Ashwin Kamath, Pankaj Kansal, Erum Kazi, Roman Levenstein, Mahesh Maddury, Alex Mastro, Siji Medaiyese, Pritesh Modi, Jack Montgomery, Nadathur Satish, Amit Nagpal, Ashwin Narasimha, Maxim Naumov, Eleanor Ozer, Jongsoo Park, Poorvaja Ramani, Harikrishna Reddy, David Reiss, Deboleena Roy, Sathish Sekar, Arushi Sharma, Pavan Shetty, Aravind Sukumaran-Rajam, Eran Tal, Mike Tsai, Shreya Varshini, Richard Wareing, Olívia Wu, Xiaolong Xie, Jinghan Yang, Hangchen Yu, Tanmay Zargar, Zitong Zeng, Feixiong Zhang, Ajit Mathews, Xun Jiao, Jiyuan Zhang, Emmanuel Menage, Truls Edvard Stokke, Mohammed Sourouri
2024 — conf
Disaggregated Multi-Tower: Topology-aware Modeling Technique for Efficient Large Scale Recommendation.
MLSys
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
2024 J jnl
Disaggregated Multi-Tower: Topology-aware Modeling Technique for Efficient Large-Scale Recommendation.
CoRR
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
2024 A* conf
Wukong: Towards a Scaling Law for Large-Scale Recommendation.
ICML
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
2024 J jnl
Wukong: Towards a Scaling Law for Large-Scale Recommendation.
CoRR
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
2023 A* conf
MTIA: First Generation Silicon Targeting Meta's Recommendation Systems.
ISCA
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
2023 J jnl
Microscaling Data Formats for Deep Learning.
CoRR
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
2023 J jnl
Shared Microexponents: A Little Shifting Goes a Long Way.
CoRR
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
2023 A* conf
With Shared Microexponents, A Little Shifting Goes a Long Way.
ISCA
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
2022 J jnl
Learning to Collide: Recommendation System Model Compression with Learned Hash Functions.
CoRR
Benjamin Ghaemmaghami, Mustafa Ozdal, Rakesh Komuravelli, Dmitriy Korchev, Dheevatsa Mudigere, Krishnakumar Nair, Maxim Naumov
2022 A* conf
Software-hardware co-design for fast and scalable training of deep learning recommendation models.
ISCA
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
2022 A conf
Supporting Massive DLRM Inference through Software Defined Memory.
ICDCS
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
2021 J jnl
Differentiable NAS Framework and Application to Ads CTR Prediction.
CoRR
Ravi Krishna, Aravind Kalaiah, Bichen Wu, Maxim Naumov, Dheevatsa Mudigere, Misha Smelyanskiy, Kurt Keutzer
2021 J jnl
High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models.
CoRR
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
2021 J jnl
Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale.
CoRR
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
2021 J jnl
Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale.
IEEE Micro
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
2021 B conf
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems.
ISIT
Antonio A. Ginart, Maxim Naumov, Dheevatsa Mudigere, Jiyan Yang, James Zou
2021 J jnl
Supporting Massive DLRM Inference Through Software Defined Memory.
CoRR
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
2020 A* conf
Building Recommender Systems with PyTorch.
KDD
Dheevatsa Mudigere, Maxim Naumov, Joe Spisak, Geeta Chauhan, Narine Kokhlikyan, Amanpreet Singh, Vedanuj Goswami
2020 A* conf
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems.
KDD
Hao-Jun Michael Shi, Dheevatsa Mudigere, Maxim Naumov, Jiyan Yang
2020 J jnl
Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems.
CoRR
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
2020 A* conf
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing.
ISCA
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
2020 A* conf
The Architectural Implications of Facebook's DNN-Based Personalized Recommendation.
HPCA
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
2020 J jnl
Time-based Sequence Model for Personalization and Recommendation Systems.
CoRR
Tigran Ishkhanov, Maxim Naumov, Xianjie Chen, Yan Zhu, Yuan Zhong, Alisson Gusatti Azzolini, Chonglin Sun, Frank Jiang, Andrey Malevich, Liang Xiong
2019 — conf
Bandana: Using Non-Volatile Memory for Storing Deep Learning Models.
SysML
Assaf Eisenman, Maxim Naumov, Darryl Gardner, Misha Smelyanskiy, Sergey Pupyrev, Kim M. Hazelwood, Asaf Cidon, Sachin Katti
2019 J jnl
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems.
CoRR
Hao-Jun Michael Shi, Dheevatsa Mudigere, Maxim Naumov, Jiyan Yang
2019 J jnl
Deep Learning Recommendation Model for Personalization and Recommendation Systems.
CoRR
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
2019 J jnl
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems.
CoRR
Antonio Ginart, Maxim Naumov, Dheevatsa Mudigere, Jiyan Yang, James Zou
2019 J jnl
On the Dimensionality of Embeddings for Sparse Features and Data.
CoRR
Maxim Naumov
2019 J jnl
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing.
CoRR
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
2019 J jnl
Spatial-Winograd Pruning Enabling Sparse Winograd Convolution.
CoRR
Jiecao Yu, Jongsoo Park, Maxim Naumov
2019 J jnl
The Architectural Implications of Facebook's DNN-based Personalized Recommendation.
CoRR
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
2018 — conf
A Block-Oriented, Parallel and Collective Approach to Sparse Indefinite Preconditioning on GPUs.
IA3@SC
Daniel Thuerck, Maxim Naumov, Michael Garland, Michael Goesele
2018 J jnl
Bandana: Using Non-volatile Memory for Storing Deep Learning Models.
CoRR
Assaf Eisenman, Maxim Naumov, Darryl Gardner, Misha Smelyanskiy, Sergey Pupyrev, Kim M. Hazelwood, Asaf Cidon, Sachin Katti
2018 J jnl
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications.
CoRR
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
2018 J jnl
On Periodic Functions as Regularizers for Quantization of Neural Networks.
CoRR
Maxim Naumov, Utku Diril, Jongsoo Park, Benjamin Ray, Jedrzej Jablonski, Andrew Tulloch
2017 J jnl
AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks.
CoRR
Aditya Devarakonda, Maxim Naumov, Michael Garland
2017 J jnl
Feedforward and Recurrent Neural Networks Backward Propagation and Hessian in Matrix Form.
CoRR
Maxim Naumov
2017 J jnl
Parallel Complexity of Forward and Backward Propagation.
CoRR
Maxim Naumov
2017 — conf
Parallel Depth-First Search for Directed Acyclic Graphs.
IA3@SC
Maxim Naumov, Alysson Vrielink, Michael Garland
2017 Misc conf
Parallel Modularity Clustering.
ICCS
Alexandre Fender, Nahid Emad, Serge G. Petiton, Maxim Naumov
2017 — conf
Parallel jaccard and related graph clustering techniques.
ScalA@SC
Alexandre Fender, Nahid Emad, Serge G. Petiton, Joe Eaton, Maxim Naumov
2015 J jnl
AmgX: A Library for GPU Accelerated Algebraic Multigrid and Preconditioned Iterative Methods.
SIAM J. Sci. Comput.
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
2011 J jnl
On the modification of an eigenvalue problem that preserves an eigenspace.
J. Comput. Appl. Math.
Maxim Naumov
2010 J jnl
A tearing-based hybrid parallel sparse linear system solver.
J. Comput. Appl. Math.
Maxim Naumov, Murat Manguoglu, Ahmed H. Sameh
2009 — ch.
Multimillion Atom Simulations with Nemo3D.
Encyclopedia of Complexity and Systems Science
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
2006 Misc conf
Semi-Lagrangian Scale Selective Two-Time-Level Scheme for Hydrostatic Atmospheric Model.
International Conference on Computational Science (1)
Andrei Bourchtein, Ludmila Bourchtein, Maxim Naumov
2006 — conf
Stability of Semi-implicit Atmospheric Models with Respect to the Reference Temperature Profile.
Numerical Methods and Applications
Andrei Bourchtein, Ludmila Bourchtein, Maxim Naumov
2004 J jnl
On correctness of the vertical discretization in numerical weather prediction models.
Appl. Math. Comput.
Andrei Bourchtein, Ludmila Bourchtein, Maxim Naumov