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Deepak Narayanan
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2020 – today
- 2024
- [c17]Wei Hao, Daniel Mendoza, Rafael Mendes, Deepak Narayanan, Amar Phanishayee, Asaf Cidon, Junfeng Yang:
MGit: A Model Versioning and Management System. ICML 2024 - [c16]Quentin Anthony
, Jacob Hatef
The Case for Co-Designing Model Architectures with Hardware. ICPP 2024: 84-96 - [i22]Quentin Anthony, Jacob Hatef, Deepak Narayanan, Stella Biderman, Stas Bekman, Junqi Yin, Aamir Shafi, Hari Subramoni, Dhabaleswar K. Panda:
The Case for Co-Designing Model Architectures with Hardware. CoRR abs/2401.14489 (2024) - [i21]Jupinder Parmar, Shrimai Prabhumoye, Joseph Jennings, Mostofa Patwary, Sandeep Subramanian, Dan Su, Chen Zhu, Deepak Narayanan, Aastha Jhunjhunwala, Ayush Dattagupta, Vibhu Jawa, Jiwei Liu, Ameya Mahabaleshwarkar, Osvald Nitski, Annika Brundyn, James Maki, Miguel Martinez, Jiaxuan You, John Kamalu, Patrick LeGresley, Denys Fridman, Jared Casper, Ashwath Aithal, Oleksii Kuchaiev, Mohammad Shoeybi, Jonathan M. Cohen, Bryan Catanzaro:
Nemotron-4 15B Technical Report. CoRR abs/2402.16819 (2024) - [i20]Roger Waleffe, Wonmin Byeon, Duncan Riach, Brandon Norick, Vijay Korthikanti, Tri Dao, Albert Gu, Ali Hatamizadeh, Sudhakar Singh, Deepak Narayanan, Garvit Kulshreshtha, Vartika Singh, Jared Casper, Jan Kautz, Mohammad Shoeybi, Bryan Catanzaro:
An Empirical Study of Mamba-based Language Models. CoRR abs/2406.07887 (2024) - [i19]Bo Adler, Niket Agarwal, Ashwath Aithal, Dong H. Anh, Pallab Bhattacharya, Annika Brundyn, Jared Casper, Bryan Catanzaro, Sharon Clay, Jonathan M. Cohen, Sirshak Das, Ayush Dattagupta, Olivier Delalleau, Leon Derczynski, Yi Dong, Daniel Egert, Ellie Evans, Aleksander Ficek, Denys Fridman, Shaona Ghosh, Boris Ginsburg, Igor Gitman, Tomasz Grzegorzek, Robert Hero, Jining Huang, Vibhu Jawa, Joseph Jennings, Aastha Jhunjhunwala, John Kamalu, Sadaf Khan, Oleksii Kuchaiev, Patrick LeGresley, Hui Li, Jiwei Liu, Zihan Liu, Eileen Long, Ameya Sunil Mahabaleshwarkar, Somshubra Majumdar, James Maki, Miguel Martinez, Maer Rodrigues de Melo, Ivan Moshkov, Deepak Narayanan, Sean Narenthiran, Jesus Navarro, Phong Nguyen, Osvald Nitski, Vahid Noroozi, Guruprasad Nutheti, Christopher Parisien, Jupinder Parmar, Mostofa Patwary, Krzysztof Pawelec, Wei Ping, Shrimai Prabhumoye, Rajarshi Roy, Trisha Saar, Vasanth Rao Naik Sabavat, Sanjeev Satheesh, Jane Polak Scowcroft, Jason Sewall, Pavel Shamis, Gerald Shen, Mohammad Shoeybi, Dave Sizer, Misha Smelyanskiy, Felipe Soares, Makesh Narsimhan Sreedhar, Dan Su, Sandeep Subramanian, Shengyang Sun, Shubham Toshniwal, Hao Wang, Zhilin Wang, Jiaxuan You, Jiaqi Zeng, Jimmy Zhang, Jing Zhang, Vivienne Zhang, Yian Zhang, Chen Zhu:
Nemotron-4 340B Technical Report. CoRR abs/2406.11704 (2024) - 2023
- [j6]Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel J. Orr, Lucia Zheng, Mert Yüksekgönül, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, Yuta Koreeda:
Holistic Evaluation of Language Models. Trans. Mach. Learn. Res. 2023 (2023) - [c15]Trevor Gale, Deepak Narayanan, Cliff Young, Matei Zaharia:
MegaBlocks: Efficient Sparse Training with Mixture-of-Experts. MLSys 2023 - [c14]Tony Lee, Michihiro Yasunaga, Chenlin Meng, Yifan Mai, Joon Sung Park, Agrim Gupta, Yunzhi Zhang, Deepak Narayanan, Hannah Teufel, Marco Bellagente, Minguk Kang, Taesung Park, Jure Leskovec, Jun-Yan Zhu, Fei-Fei Li, Jiajun Wu, Stefano Ermon, Percy Liang:
Holistic Evaluation of Text-to-Image Models. NeurIPS 2023 - [c13]Deepak Narayanan, Keshav Santhanam, Peter Henderson, Rishi Bommasani, Tony Lee, Percy Liang:
Cheaply Estimating Inference Efficiency Metrics for Autoregressive Transformer Models. NeurIPS 2023 - [c12]Sultan Mahmud Sajal, Luke Marshall, Beibin Li, Shandan Zhou, Abhisek Pan, Konstantina Mellou, Deepak Narayanan, Timothy Zhu, David Dion, Thomas Moscibroda, Ishai Menache:
Kerveros: Efficient and Scalable Cloud Admission Control. OSDI 2023: 227-245 - [i18]Deepak Narayanan, Keshav Santhanam, Peter Henderson, Rishi Bommasani, Tony Lee, Percy Liang:
Cheaply Evaluating Inference Efficiency Metrics for Autoregressive Transformer APIs. CoRR abs/2305.02440 (2023) - [i17]Wei Hao, Daniel Mendoza, Rafael da Silva, Deepak Narayanan, Amar Phanishayee:
MGit: A Model Versioning and Management System. CoRR abs/2307.07507 (2023) - [i16]Tony Lee, Michihiro Yasunaga, Chenlin Meng, Yifan Mai, Joon Sung Park, Agrim Gupta, Yunzhi Zhang, Deepak Narayanan, Hannah Benita Teufel, Marco Bellagente, Minguk Kang, Taesung Park, Jure Leskovec, Jun-Yan Zhu, Li Fei-Fei, Jiajun Wu, Stefano Ermon, Percy Liang:
Holistic Evaluation of Text-To-Image Models. CoRR abs/2311.04287 (2023) - [i15]Ankit Bhardwaj, Amar Phanishayee, Deepak Narayanan, Mihail Tarta, Ryan Stutsman:
Packrat: Automatic Reconfiguration for Latency Minimization in CPU-based DNN Serving. CoRR abs/2311.18174 (2023) - 2022
- [j5]Akshay Agrawal, Stephen P. Boyd, Deepak Narayanan, Fiodar Kazhamiaka, Matei Zaharia
Allocation of fungible resources via a fast, scalable price discovery method. Math. Program. Comput. 14(3): 593-622 (2022) - [i14]Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel J. Orr, Lucia Zheng, Mert Yüksekgönül
Holistic Evaluation of Language Models. CoRR abs/2211.09110 (2022) - [i13]Trevor Gale, Deepak Narayanan, Cliff Young, Matei Zaharia
MegaBlocks: Efficient Sparse Training with Mixture-of-Experts. CoRR abs/2211.15841 (2022) - 2021
- [b1]Deepak Narayanan:
Resource-efficient execution of deep learning computations. Stanford University, USA, 2021 - [c11]Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, Xie Chen, Matei Zaharia:
Memory-Efficient Pipeline-Parallel DNN Training. ICML 2021: 7937-7947 - [c10]Jakub Tarnawski, Deepak Narayanan, Amar Phanishayee:
Piper: Multidimensional Planner for DNN Parallelization. NeurIPS 2021: 24829-24840 - [c9]Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, Amar Phanishayee, Matei Zaharia
Efficient large-scale language model training on GPU clusters using megatron-LM. SC 2021: 58 - [c8]Deepak Narayanan, Fiodar Kazhamiaka, Firas Abuzaid, Peter Kraft, Akshay Agrawal, Srikanth Kandula, Stephen P. Boyd, Matei Zaharia
Solving Large-Scale Granular Resource Allocation Problems Efficiently with POP. SOSP 2021: 521-537 - [i12]Akshay Agrawal, Stephen P. Boyd, Deepak Narayanan, Fiodar Kazhamiaka, Matei Zaharia:
Allocation of Fungible Resources via a Fast, Scalable Price Discovery Method. CoRR abs/2104.00282 (2021) - [i11]Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, Amar Phanishayee, Matei Zaharia:
Efficient Large-Scale Language Model Training on GPU Clusters. CoRR abs/2104.04473 (2021) - [i10]Deepak Narayanan, Fiodar Kazhamiaka, Firas Abuzaid, Peter Kraft, Matei Zaharia:
Don't Give Up on Large Optimization Problems; POP Them! CoRR abs/2104.06513 (2021) - [i9]Deepak Narayanan, Fiodar Kazhamiaka, Firas Abuzaid, Peter Kraft, Akshay Agrawal, Srikanth Kandula, Stephen P. Boyd, Matei Zaharia:
Solving Large-Scale Granular Resource Allocation Problems Efficiently with POP. CoRR abs/2110.11927 (2021) - 2020
- [j4]Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia
A Demonstration of Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference. Proc. VLDB Endow. 13(12): 2833-2836 (2020) - [c7]Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia:
Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference. MLSys 2020 - [c6]Peter Mattson, Christine Cheng, Gregory F. Diamos, Cody Coleman, Paulius Micikevicius, David A. Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debo Dutta, Udit Gupta, Kim M. Hazelwood, Andy Hock, Xinyuan Huang, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia:
MLPerf Training Benchmark. MLSys 2020 - [c5]Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, Matei Zaharia:
Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads. OSDI 2020: 481-498 - [c4]Gina Yuan, Shoumik Palkar, Deepak Narayanan, Matei Zaharia:
Offload Annotations: Bringing Heterogeneous Computing to Existing Libraries and Workloads. USENIX ATC 2020: 293-306 - [i8]Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, Xie Chen, Matei Zaharia:
Memory-Efficient Pipeline-Parallel DNN Training. CoRR abs/2006.09503 (2020) - [i7]Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, Matei Zaharia:
Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads. CoRR abs/2008.09213 (2020)
2010 – 2019
- 2019
- [j3]Cody Coleman, Daniel Kang
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark. ACM SIGOPS Oper. Syst. Rev. 53(1): 14-25 (2019) - [c3]Deepak Narayanan, Aaron Harlap, Amar Phanishayee, Vivek Seshadri, Nikhil R. Devanur, Gregory R. Ganger, Phillip B. Gibbons, Matei Zaharia
PipeDream: generalized pipeline parallelism for DNN training. SOSP 2019: 1-15 - [i6]Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia:
Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference. CoRR abs/1906.01974 (2019) - [i5]Peter Mattson, Christine Cheng, Cody Coleman, Greg Diamos, Paulius Micikevicius, David A. Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debojyoti Dutta, Udit Gupta, Kim M. Hazelwood, Andrew Hock, Xinyuan Huang, Bill Jia, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Guokai Ma, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia:
MLPerf Training Benchmark. CoRR abs/1910.01500 (2019) - 2018
- [j2]Shoumik Palkar, James Thomas, Deepak Narayanan, Pratiksha Thaker, Rahul Palamuttam, Parimarjan Negi, Anil Shanbhag, Malte Schwarzkopf, Holger Pirk, Saman P. Amarasinghe, Samuel Madden, Matei Zaharia
Evaluating End-to-End Optimization for Data Analytics Applications in Weld. Proc. VLDB Endow. 11(9): 1002-1015 (2018) - [j1]Firas Abuzaid, Peter Bailis, Jialin Ding, Edward Gan, Samuel Madden, Deepak Narayanan, Kexin Rong
MacroBase: Prioritizing Attention in Fast Data. ACM Trans. Database Syst. 43(4): 15:1-15:45 (2018) - [i4]Cody Coleman, Daniel Kang, Deepak Narayanan, Luigi Nardi, Tian Zhao, Jian Zhang, Peter Bailis, Kunle Olukotun, Christopher Ré, Matei Zaharia:
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark. CoRR abs/1806.01427 (2018) - [i3]Aaron Harlap, Deepak Narayanan, Amar Phanishayee, Vivek Seshadri, Nikhil R. Devanur, Gregory R. Ganger, Phillip B. Gibbons:
PipeDream: Fast and Efficient Pipeline Parallel DNN Training. CoRR abs/1806.03377 (2018) - 2017
- [c2]Shoumik Palkar, James Thomas, Anil Shanbhag, Deepak Narayanan, Holger Pirk, Malte Schwarzkopf, Saman P. Amarasinghe, Matei Zaharia:
A Common Runtime for High Performance Data Analysis. CIDR 2017 - [c1]Peter Bailis, Edward Gan
MacroBase: Prioritizing Attention in Fast Data. SIGMOD Conference 2017: 541-556 - [i2]Shoumik Palkar, James Thomas, Deepak Narayanan, Anil Shanbhag, Rahul Palamuttam, Holger Pirk, Malte Schwarzkopf, Saman P. Amarasinghe, Samuel Madden, Matei Zaharia:
Weld: Rethinking the Interface Between Data-Intensive Applications. CoRR abs/1709.06416 (2017) - 2016
- [i1]Peter Bailis, Deepak Narayanan, Samuel Madden:
MacroBase: Analytic Monitoring for the Internet of Things. CoRR abs/1603.00567 (2016)
Coauthor Index
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