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Dheevatsa Mudigere
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2020 – today
- 2024
- [c24]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 - [c23]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: 667-682 - [i33]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) - 2023
- [c22]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: 739-767 - [i32]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 abs/2305.01515 (2023) - [i31]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 abs/2309.06497 (2023) - 2022
- [c21]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 - [c20]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 - [c19]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: 929-943 - [c18]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: 267-284 - [c17]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: 70:1-70:14 - [i30]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 abs/2202.00433 (2022) - [i29]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 abs/2203.11014 (2022) - [i28]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
- [c16]Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul:
SEERL: Sample Efficient Ensemble Reinforcement Learning. AAMAS 2021: 1100-1108 - [c15]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 - [i27]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) - [i26]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) - [i25]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
- [c14]Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul:
ERLP: Ensembles of Reinforcement Learning Policies (Student Abstract). AAAI 2020: 13905-13906 - [c13]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: 2634-2644 - [c12]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: 33-42 - [c11]Udit Gupta
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
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
Building Recommender Systems with PyTorch. KDD 2020: 3525-3526 - [i24]Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul:
SEERL: Sample Efficient Ensemble Reinforcement Learning. CoRR abs/2001.05209 (2020) - [i23]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) - [i22]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 abs/2010.08679 (2020)
2010 – 2019
- 2019
- [i21]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 abs/1905.12322 (2019) - [i20]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) - [i19]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) - [i18]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) - [i17]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) - [i16]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
- [c7]Anirban Santara, Abhishek Naik, Balaraman Ravindran, Dipankar Das, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul:
RAIL: Risk-Averse Imitation Learning. AAMAS 2018: 2062-2063 - [c6]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 - [c5]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: 619-628 - [i15]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 abs/1801.08030 (2018) - [i14]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 abs/1802.00930 (2018) - [i13]Raghu Bollapragada, Dheevatsa Mudigere, Jorge Nocedal, Hao-Jun Michael Shi, Ping Tak Peter Tang:
A Progressive Batching L-BFGS Method for Machine Learning. CoRR abs/1802.05374 (2018) - [i12]Dharma Teja Vooturi, Dheevatsa Mudigere, Sasikanth Avancha:
Hierarchical Block Sparse Neural Networks. CoRR abs/1808.03420 (2018) - [i11]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 abs/1810.11507 (2018) - 2017
- [c4]Xi He, Dheevatsa Mudigere, Mikhail Smelyanskiy, Martin Takác:
Distributed Hessian-Free Optimization for Deep Neural Network. AAAI Workshops 2017 - [c3]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 - [i10]Naveen Mellempudi, Abhisek Kundu, Dipankar Das, Dheevatsa Mudigere, Bharat Kaul:
Mixed Low-precision Deep Learning Inference using Dynamic Fixed Point. CoRR abs/1701.08978 (2017) - [i9]Naveen Mellempudi, Abhisek Kundu, Dheevatsa Mudigere, Dipankar Das, Bharat Kaul, Pradeep Dubey:
Ternary Neural Networks with Fine-Grained Quantization. CoRR abs/1705.01462 (2017) - [i8]Abhisek Kundu, Kunal Banerjee, Naveen Mellempudi, Dheevatsa Mudigere, Dipankar Das, Bharat Kaul, Pradeep Dubey:
Ternary Residual Networks. CoRR abs/1707.04679 (2017) - [i7]Anirban Santara, Abhishek Naik, Balaraman Ravindran, Dipankar Das, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul:
RAIL: Risk-Averse Imitation Learning. CoRR abs/1707.06658 (2017) - 2016
- [i6]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 abs/1602.06709 (2016) - [i5]Xi He, Dheevatsa Mudigere, Mikhail Smelyanskiy, Martin Takác:
Large Scale Distributed Hessian-Free Optimization for Deep Neural Network. CoRR abs/1606.00511 (2016) - [i4]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 abs/1609.04836 (2016) - 2015
- [j2]S. N. Omkar, Dheevatsa Mudigere, J. Senthilnath
Identification of Helicopter Dynamics based on Flight Data using Nature Inspired Techniques. Int. J. Appl. Metaheuristic Comput. 6(3): 38-52 (2015) - [c2]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: 723-732 - [c1]Jongsoo Park, Mikhail Smelyanskiy, Ulrike Meier Yang
High-performance algebraic multigrid solver optimized for multi-core based distributed parallel systems. SC 2015: 54:1-54:12 - 2014
- [i3]S. N. Omkar, Dheevatsa Mudigere, J. Senthilnath, M. Vijaya Kumar:
Identification of Helicopter Dynamics based on Flight Data using Nature Inspired Techniques. CoRR abs/1411.3251 (2014) - 2011
- [j1]G. Narayana Naik
Nature inspired optimization techniques for the design optimization of laminated composite structures using failure criteria. Expert Syst. Appl. 38(3): 2489-2499 (2011) - 2010
- [i2]Sisir Koppaka, Dheevatsa Mudigere, Srihari Narasimhan, Babu Narayanan:
Fast Histograms using Adaptive CUDA Streams. CoRR abs/1011.0235 (2010) - [i1]Michael Bader, Hans-Joachim Bungartz, Dheevatsa Mudigere, Srihari Narasimhan, Babu Narayanan:
Fast GPGPU Data Rearrangement Kernels using CUDA. CoRR abs/1011.3583 (2010)
Coauthor Index
Mikhail Smelyanskiy
aka: Misha Smelyanskiy
aka: Misha Smelyanskiy
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