Dheevatsa Mudigere

62 papers A* 8A 7B 1Misc 2Journal 38Unranked 6

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Beyond the Buzz: A Pragmatic Take on Inference Disaggregation. CoRR Tiyasa Mitra, Ritika Borkar, Nidhi Bhatia, Ramon Matas, Shivam Raj, Dheevatsa Mudigere, Ritchie Zhao, Maximilian Golub, Arpan Dutta, Sailaja Madduri, Dharmesh Jani, Brian Pharris, Bita Darvish Rouhani
2025	J	`jnl`	Helix Parallelism: Rethinking Sharding Strategies for Interactive Multi-Million-Token LLM Decoding. CoRR Nidhi Bhatia, Ankit More, Ritika Borkar, Tiyasa Mitra, Ramon Matas, Ritchie Zhao, Maximilian Golub, Dheevatsa Mudigere, Brian Pharris, Bita Darvish Rouhani
2025	J	`jnl`	Nonuniform-Tensor-Parallelism: Mitigating GPU failure impact for Scaled-up LLM Training. CoRR Daiyaan Arfeen, Dheevatsa Mudigere, Ankit More, Bhargava Gopireddy, Ahmet Inci, Gregory R. Ganger
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	—	`conf`	OPER: Optimality-Guided Embedding Table Parallelization for Large-scale Recommendation Model. USENIX ATC Zheng Wang, Yuke Wang, Boyuan Feng, Guyue Huang, Dheevatsa Mudigere, Bharath Muthiah, Ang Li, Yufei Ding
2023	J	`jnl`	A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale. CoRR Hao-Jun Michael Shi, Tsung-Hsien Lee, Shintaro Iwasaki, Jose Gallego-Posada, Zhijing Li, Kaushik Rangadurai, Dheevatsa Mudigere, Michael Rabbat
2023	J	`jnl`	MTrainS: Improving DLRM training efficiency using heterogeneous memories. CoRR Hiwot Tadese Kassa, Paul Johnson, Jason Akers, Mrinmoy Ghosh, Andrew Tulloch, Dheevatsa Mudigere, Jongsoo Park, Xing Liu, Ronald G. Dreslinski, Ehsan K. Ardestani
2023	Misc	`conf`	TopoOpt: Co-optimizing Network Topology and Parallelization Strategy for Distributed Training Jobs. NSDI Weiyang Wang, Moein Khazraee, Zhizhen Zhong, Manya Ghobadi, Zhihao Jia, Dheevatsa Mudigere, Ying Zhang, Anthony Kewitsch
2022	Misc	`conf`	Check-N-Run: a Checkpointing System for Training Deep Learning Recommendation Models. NSDI Assaf Eisenman, Kiran Kumar Matam, Steven Ingram, Dheevatsa Mudigere, Raghuraman Krishnamoorthi, Krishnakumar Nair, Misha Smelyanskiy, Murali Annavaram
2022	J	`jnl`	DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction. CoRR Buyun Zhang, Liang Luo, Xi Liu, Jay Li, ZeLiang Chen, Weilin Zhang, Xiaohan Wei, Yuchen Hao, Michael Tsang, Wenjun Wang, Yang Liu, Huayu Li, Yasmine Badr, Jongsoo Park, Jiyan Yang, Dheevatsa Mudigere, Ellie Wen
2022	A	`conf`	EL-Rec: Efficient Large-Scale Recommendation Model Training via Tensor-Train Embedding Table. SC Zheng Wang, Yuke Wang, Boyuan Feng, Dheevatsa Mudigere, Bharath Muthiah, Yufei Ding
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
2022	J	`jnl`	TopoOpt: Optimizing the Network Topology for Distributed DNN Training. CoRR Weiyang Wang, Moein Khazraee, Zhizhen Zhong, Zhijao Jia, Dheevatsa Mudigere, Ying Zhang, Anthony Kewitsch, Manya Ghobadi
2022	A*	`conf`	Unity: Accelerating DNN Training Through Joint Optimization of Algebraic Transformations and Parallelization. OSDI Colin Unger, Zhihao Jia, Wei Wu, Sina Lin, Mandeep Baines, Carlos Efrain Quintero Narvaez, Vinay Ramakrishnaiah, Nirmal Prajapati, Patrick S. McCormick, Jamaludin Mohd-Yusof, Xi Luo, Dheevatsa Mudigere, Jongsoo Park, Misha Smelyanskiy, Alex Aiken
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	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	A	`conf`	SEERL: Sample Efficient Ensemble Reinforcement Learning. AAMAS Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul
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	J	`jnl`	Check-N-Run: A Checkpointing System for Training Recommendation Models. CoRR Assaf Eisenman, Kiran Kumar Matam, Steven Ingram, Dheevatsa Mudigere, Raghuraman Krishnamoorthi, Murali Annavaram, Krishnakumar Nair, Misha Smelyanskiy
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`	ERLP: Ensembles of Reinforcement Learning Policies (Student Abstract). AAAI Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul
2020	A	`conf`	Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy. AISTATS Majid Jahani, Xi He, Chenxin Ma, Aryan Mokhtari, Dheevatsa Mudigere, Alejandro Ribeiro, Martin Takác
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	J	`jnl`	SEERL: Sample Efficient Ensemble Reinforcement Learning. CoRR Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul
2020	—	`conf`	Scalable Distributed Training of Recommendation Models: An ASTRA-SIM + NS3 case-study with TCP/IP transport. Hot Interconnects Saeed Rashidi, Pallavi Shurpali, Srinivas Sridharan, Naader Hassani, Dheevatsa Mudigere, Krishnakumar Nair, Misha Smelyanskiy, Tushar Krishna
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
2019	J	`jnl`	A Study of BFLOAT16 for Deep Learning Training. CoRR Dhiraj D. Kalamkar, Dheevatsa Mudigere, Naveen Mellempudi, Dipankar Das, Kunal Banerjee, Sasikanth Avancha, Dharma Teja Vooturi, Nataraj Jammalamadaka, Jianyu Huang, Hector Yuen, Jiyan Yang, Jongsoo Park, Alexander Heinecke, Evangelos Georganas, Sudarshan Srinivasan, Abhisek Kundu, Misha Smelyanskiy, Bharat Kaul, Pradeep Dubey
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`	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`	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	A*	`conf`	A Progressive Batching L-BFGS Method for Machine Learning. ICML Raghu Bollapragada, Dheevatsa Mudigere, Jorge Nocedal, Hao-Jun Michael Shi, Ping Tak Peter Tang
2018	J	`jnl`	A Progressive Batching L-BFGS Method for Machine Learning. CoRR Raghu Bollapragada, Dheevatsa Mudigere, Jorge Nocedal, Hao-Jun Michael Shi, Ping Tak Peter Tang
2018	J	`jnl`	Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy. CoRR Majid Jahani, Xi He, Chenxin Ma, Aryan Mokhtari, Dheevatsa Mudigere, Alejandro Ribeiro, Martin Takác
2018	J	`jnl`	Hierarchical Block Sparse Neural Networks. CoRR Dharma Teja Vooturi, Dheevatsa Mudigere, Sasikanth Avancha
2018	—	`conf`	Mixed Precision Training of Convolutional Neural Networks using Integer Operations. ICLR (Poster) Dipankar Das, Naveen Mellempudi, Dheevatsa Mudigere, Dhiraj D. Kalamkar, Sasikanth Avancha, Kunal Banerjee, Srinivas Sridharan, Karthik Vaidyanathan, Bharat Kaul, Evangelos Georganas, Alexander Heinecke, Pradeep Dubey, Jesús Corbal, Nikita Shustrov, Roman Dubtsov, Evarist Fomenko, Vadim O. Pirogov
2018	J	`jnl`	Mixed Precision Training of Convolutional Neural Networks using Integer Operations. CoRR Dipankar Das, Naveen Mellempudi, Dheevatsa Mudigere, Dhiraj D. Kalamkar, Sasikanth Avancha, Kunal Banerjee, Srinivas Sridharan, Karthik Vaidyanathan, Bharat Kaul, Evangelos Georganas, Alexander Heinecke, Pradeep Dubey, Jesús Corbal, Nikita Shustrov, Roman Dubtsov, Evarist Fomenko, Vadim O. Pirogov
2018	J	`jnl`	On Scale-out Deep Learning Training for Cloud and HPC. CoRR Srinivas Sridharan, Karthikeyan Vaidyanathan, Dhiraj D. Kalamkar, Dipankar Das, Mikhail E. Smorkalov, Mikhail Shiryaev, Dheevatsa Mudigere, Naveen Mellempudi, Sasikanth Avancha, Bharat Kaul, Pradeep Dubey
2018	A	`conf`	RAIL: Risk-Averse Imitation Learning. AAMAS Anirban Santara, Abhishek Naik, Balaraman Ravindran, Dipankar Das, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul
2017	—	`conf`	Distributed Hessian-Free Optimization for Deep Neural Network. AAAI Workshops Xi He, Dheevatsa Mudigere, Mikhail Smelyanskiy, Martin Takác
2017	J	`jnl`	Mixed Low-precision Deep Learning Inference using Dynamic Fixed Point. CoRR Naveen Mellempudi, Abhisek Kundu, Dipankar Das, Dheevatsa Mudigere, Bharat Kaul
2017	—	`conf`	On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima. ICLR Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, Ping Tak Peter Tang
2017	J	`jnl`	RAIL: Risk-Averse Imitation Learning. CoRR Anirban Santara, Abhishek Naik, Balaraman Ravindran, Dipankar Das, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul
2017	J	`jnl`	Ternary Neural Networks with Fine-Grained Quantization. CoRR Naveen Mellempudi, Abhisek Kundu, Dheevatsa Mudigere, Dipankar Das, Bharat Kaul, Pradeep Dubey
2017	J	`jnl`	Ternary Residual Networks. CoRR Abhisek Kundu, Kunal Banerjee, Naveen Mellempudi, Dheevatsa Mudigere, Dipankar Das, Bharat Kaul, Pradeep Dubey
2016	J	`jnl`	Distributed Deep Learning Using Synchronous Stochastic Gradient Descent. CoRR Dipankar Das, Sasikanth Avancha, Dheevatsa Mudigere, Karthikeyan Vaidyanathan, Srinivas Sridharan, Dhiraj D. Kalamkar, Bharat Kaul, Pradeep Dubey
2016	J	`jnl`	Large Scale Distributed Hessian-Free Optimization for Deep Neural Network. CoRR Xi He, Dheevatsa Mudigere, Mikhail Smelyanskiy, Martin Takác
2016	J	`jnl`	On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima. CoRR Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, Ping Tak Peter Tang
2015	A	`conf`	Exploring Shared-Memory Optimizations for an Unstructured Mesh CFD Application on Modern Parallel Systems. IPDPS Dheevatsa Mudigere, Srinivas Sridharan, Anand M. Deshpande, Jongsoo Park, Alexander Heinecke, Mikhail Smelyanskiy, Bharat Kaul, Pradeep Dubey, Dinesh K. Kaushik, David E. Keyes
2015	A	`conf`	High-performance algebraic multigrid solver optimized for multi-core based distributed parallel systems. SC Jongsoo Park, Mikhail Smelyanskiy, Ulrike Meier Yang, Dheevatsa Mudigere, Pradeep Dubey
2015	J	`jnl`	Identification of Helicopter Dynamics based on Flight Data using Nature Inspired Techniques. Int. J. Appl. Metaheuristic Comput. S. N. Omkar, Dheevatsa Mudigere, J. Senthilnath, M. Vijaya Kumar
2014	J	`jnl`	Identification of Helicopter Dynamics based on Flight Data using Nature Inspired Techniques. CoRR S. N. Omkar, Dheevatsa Mudigere, J. Senthilnath, M. Vijaya Kumar
2011	J	`jnl`	Nature inspired optimization techniques for the design optimization of laminated composite structures using failure criteria. Expert Syst. Appl. G. Narayana Naik, S. N. Omkar, Dheevatsa Mudigere, S. Gopalakrishnan
2010	J	`jnl`	Fast GPGPU Data Rearrangement Kernels using CUDA CoRR Michael Bader, Hans-Joachim Bungartz, Dheevatsa Mudigere, Srihari Narasimhan, Babu Narayanan
2010	J	`jnl`	Fast Histograms using Adaptive CUDA Streams CoRR Sisir Koppaka, Dheevatsa Mudigere, Srihari Narasimhan, Babu Narayanan