Dheevatsa Mudigere

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 52

Top Topics

Recommendation Systems

Parallelization Strategy

Top Venues

Publications

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 (2024)
Zheng Wang, Yuke Wang, Boyuan Feng, Guyue Huang, Dheevatsa Mudigere, Bharath Muthiah, Ang Li, Yufei Ding
OPER: Optimality-Guided Embedding Table Parallelization for Large-scale Recommendation Model. USENIX ATC (2024)
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)
Hao-Jun Michael Shi, Tsung-Hsien Lee, Shintaro Iwasaki, Jose Gallego-Posada, Zhijing Li, Kaushik Rangadurai, Dheevatsa Mudigere, Michael Rabbat
A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale. CoRR (2023)
Weiyang Wang, Moein Khazraee, Zhizhen Zhong, Manya Ghobadi, Zhihao Jia, Dheevatsa Mudigere, Ying Zhang, Anthony Kewitsch
TopoOpt: Co-optimizing Network Topology and Parallelization Strategy for Distributed Training Jobs. NSDI (2023)
Hiwot Tadese Kassa, Paul Johnson, Jason Akers, Mrinmoy Ghosh, Andrew Tulloch, Dheevatsa Mudigere, Jongsoo Park, Xing Liu, Ronald G. Dreslinski, Ehsan K. Ardestani
MTrainS: Improving DLRM training efficiency using heterogeneous memories. CoRR (2023)
Zheng Wang, Yuke Wang, Boyuan Feng, Dheevatsa Mudigere, Bharath Muthiah, Yufei Ding
EL-Rec: Efficient Large-Scale Recommendation Model Training via Tensor-Train Embedding Table. SC (2022)
Assaf Eisenman, Kiran Kumar Matam, Steven Ingram, Dheevatsa Mudigere, Raghuraman Krishnamoorthi, Krishnakumar Nair, Misha Smelyanskiy, Murali Annavaram
Check-N-Run: a Checkpointing System for Training Deep Learning Recommendation Models. NSDI (2022)
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
DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction. CoRR (2022)
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
Unity: Accelerating DNN Training Through Joint Optimization of Algebraic Transformations and Parallelization. OSDI (2022)
Weiyang Wang, Moein Khazraee, Zhizhen Zhong, Zhijao Jia, Dheevatsa Mudigere, Ying Zhang, Anthony Kewitsch, Manya Ghobadi
TopoOpt: Optimizing the Network Topology for Distributed DNN Training. CoRR (2022)
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)
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 (2022)
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)
Antonio A. Ginart, Maxim Naumov, Dheevatsa Mudigere, Jiyan Yang, James Zou
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems. ISIT (2021)
Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul
SEERL: Sample Efficient Ensemble Reinforcement Learning. AAMAS (2021)
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 (2021)
Ravi Krishna, Aravind Kalaiah, Bichen Wu, Maxim Naumov, Dheevatsa Mudigere, Misha Smelyanskiy, Kurt Keutzer
Differentiable NAS Framework and Application to Ads CTR Prediction. CoRR (2021)
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 (2021)
Assaf Eisenman, Kiran Kumar Matam, Steven Ingram, Dheevatsa Mudigere, Raghuraman Krishnamoorthi, Murali Annavaram, Krishnakumar Nair, Misha Smelyanskiy
Check-N-Run: A Checkpointing System for Training Recommendation Models. CoRR (2020)
Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul
SEERL: Sample Efficient Ensemble Reinforcement Learning. CoRR (2020)
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)
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 (2020)
Hao-Jun Michael Shi, Dheevatsa Mudigere, Maxim Naumov, Jiyan Yang
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems. KDD (2020)
Majid Jahani, Xi He, Chenxin Ma, Aryan Mokhtari, Dheevatsa Mudigere, Alejandro Ribeiro, Martin Takác
Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy. AISTATS (2020)
Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul
ERLP: Ensembles of Reinforcement Learning Policies (Student Abstract). AAAI (2020)
Saeed Rashidi, Pallavi Shurpali, Srinivas Sridharan, Naader Hassani, Dheevatsa Mudigere, Krishnakumar Nair, Misha Smelyanski, Tushar Krishna
Scalable Distributed Training of Recommendation Models: An ASTRA-SIM + NS3 case-study with TCP/IP transport. Hot Interconnects (2020)
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)
Dheevatsa Mudigere, Maxim Naumov, Joe Spisak, Geeta Chauhan, Narine Kokhlikyan, Amanpreet Singh, Vedanuj Goswami
Building Recommender Systems with PyTorch. KDD (2020)
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 (2019)
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 (2019)
Hao-Jun Michael Shi, Dheevatsa Mudigere, Maxim Naumov, Jiyan Yang
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems. CoRR (2019)
Antonio Ginart, Maxim Naumov, Dheevatsa Mudigere, Jiyan Yang, James Zou
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems. CoRR (2019)
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 (2019)
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
A Study of BFLOAT16 for Deep Learning Training. CoRR (2019)
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
Mixed Precision Training of Convolutional Neural Networks using Integer Operations. ICLR (Poster) (2018)
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
Mixed Precision Training of Convolutional Neural Networks using Integer Operations. CoRR (2018)
Dharma Teja Vooturi, Dheevatsa Mudigere, Sasikanth Avancha
Hierarchical Block Sparse Neural Networks. CoRR (2018)
Srinivas Sridharan, Karthikeyan Vaidyanathan, Dhiraj D. Kalamkar, Dipankar Das, Mikhail E. Smorkalov, Mikhail Shiryaev, Dheevatsa Mudigere, Naveen Mellempudi, Sasikanth Avancha, Bharat Kaul, Pradeep Dubey
On Scale-out Deep Learning Training for Cloud and HPC. CoRR (2018)
Raghu Bollapragada, Dheevatsa Mudigere, Jorge Nocedal, Hao-Jun Michael Shi, Ping Tak Peter Tang
A Progressive Batching L-BFGS Method for Machine Learning. CoRR (2018)
Majid Jahani, Xi He, Chenxin Ma, Aryan Mokhtari, Dheevatsa Mudigere, Alejandro Ribeiro, Martin Takác
Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy. CoRR (2018)
Anirban Santara, Abhishek Naik, Balaraman Ravindran, Dipankar Das, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul
RAIL: Risk-Averse Imitation Learning. AAMAS (2018)
Raghu Bollapragada, Dheevatsa Mudigere, Jorge Nocedal, Hao-Jun Michael Shi, Ping Tak Peter Tang
A Progressive Batching L-BFGS Method for Machine Learning. ICML (2018)
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, Ping Tak Peter Tang
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima. ICLR (2017)
Xi He, Dheevatsa Mudigere, Mikhail Smelyanskiy, Martin Takác
Distributed Hessian-Free Optimization for Deep Neural Network. AAAI Workshops (2017)
Naveen Mellempudi, Abhisek Kundu, Dipankar Das, Dheevatsa Mudigere, Bharat Kaul
Mixed Low-precision Deep Learning Inference using Dynamic Fixed Point. CoRR (2017)
Anirban Santara, Abhishek Naik, Balaraman Ravindran, Dipankar Das, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul
RAIL: Risk-Averse Imitation Learning. CoRR (2017)
Naveen Mellempudi, Abhisek Kundu, Dheevatsa Mudigere, Dipankar Das, Bharat Kaul, Pradeep Dubey
Ternary Neural Networks with Fine-Grained Quantization. CoRR (2017)
Abhisek Kundu, Kunal Banerjee, Naveen Mellempudi, Dheevatsa Mudigere, Dipankar Das, Bharat Kaul, Pradeep Dubey
Ternary Residual Networks. CoRR (2017)
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, Ping Tak Peter Tang
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima. CoRR (2016)
Dipankar Das, Sasikanth Avancha, Dheevatsa Mudigere, Karthikeyan Vaidyanathan, Srinivas Sridharan, Dhiraj D. Kalamkar, Bharat Kaul, Pradeep Dubey
Distributed Deep Learning Using Synchronous Stochastic Gradient Descent. CoRR (2016)
Xi He, Dheevatsa Mudigere, Mikhail Smelyanskiy, Martin Takác
Large Scale Distributed Hessian-Free Optimization for Deep Neural Network. CoRR (2016)
S. N. Omkar, Dheevatsa Mudigere, J. Senthilnath, M. Vijaya Kumar
Identification of Helicopter Dynamics based on Flight Data using Nature Inspired Techniques. Int. J. Appl. Metaheuristic Comput. 6 (3) (2015)
Dheevatsa Mudigere, Srinivas Sridharan, Anand M. Deshpande, Jongsoo Park, Alexander Heinecke, Mikhail Smelyanskiy, Bharat Kaul, Pradeep Dubey, Dinesh K. Kaushik, David E. Keyes
Exploring Shared-Memory Optimizations for an Unstructured Mesh CFD Application on Modern Parallel Systems. IPDPS (2015)
Jongsoo Park, Mikhail Smelyanskiy, Ulrike Meier Yang, Dheevatsa Mudigere, Pradeep Dubey
High-performance algebraic multigrid solver optimized for multi-core based distributed parallel systems. SC (2015)
S. N. Omkar, Dheevatsa Mudigere, J. Senthilnath, M. Vijaya Kumar
Identification of Helicopter Dynamics based on Flight Data using Nature Inspired Techniques. CoRR (2014)
G. Narayana Naik, S. N. Omkar, Dheevatsa Mudigere, S. Gopalakrishnan
Nature inspired optimization techniques for the design optimization of laminated composite structures using failure criteria. Expert Syst. Appl. 38 (3) (2011)
Michael Bader, Hans-Joachim Bungartz, Dheevatsa Mudigere, Srihari Narasimhan, Babu Narayanan
Fast GPGPU Data Rearrangement Kernels using CUDA CoRR (2010)
Sisir Koppaka, Dheevatsa Mudigere, Srihari Narasimhan, Babu Narayanan
Fast Histograms using Adaptive CUDA Streams CoRR (2010)