Deepak Narayanan

46 papers A* 10A 2B 1Journal 27Unranked 5

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Auto-reconfiguration for Latency Minimization in CPU-based DNN Serving. ICML Ankit Bhardwaj, Amar Phanishayee, Deepak Narayanan, Ryan Stutsman
2024	J	`jnl`	An Empirical Study of Mamba-based Language Models. CoRR 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
2024	A*	`conf`	MGit: A Model Versioning and Management System. ICML Wei Hao, Daniel Mendoza, Rafael Mendes, Deepak Narayanan, Amar Phanishayee, Asaf Cidon, Junfeng Yang
2024	J	`jnl`	Nemotron-4 15B Technical Report. CoRR 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
2024	B	`conf`	The Case for Co-Designing Model Architectures with Hardware. ICPP Quentin Anthony, Jacob Hatef, Deepak Narayanan, Stella Biderman, Stas Bekman, Junqi Yin, Aamir Shafi, Hari Subramoni, Dhabaleswar K. Panda
2024	J	`jnl`	The Case for Co-Designing Model Architectures with Hardware. CoRR Quentin Anthony, Jacob Hatef, Deepak Narayanan, Stella Biderman, Stas Bekman, Junqi Yin, Aamir Shafi, Hari Subramoni, Dhabaleswar K. Panda
2023	A*	`conf`	Cheaply Estimating Inference Efficiency Metrics for Autoregressive Transformer Models. NeurIPS Deepak Narayanan, Keshav Santhanam, Peter Henderson, Rishi Bommasani, Tony Lee, Percy Liang
2023	J	`jnl`	Cheaply Evaluating Inference Efficiency Metrics for Autoregressive Transformer APIs. CoRR Deepak Narayanan, Keshav Santhanam, Peter Henderson, Rishi Bommasani, Tony Lee, Percy Liang
2023	J	`jnl`	Holistic Evaluation of Language Models. Trans. Mach. Learn. Res. 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
2023	J	`jnl`	Holistic Evaluation of Text-To-Image Models. CoRR 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
2023	A*	`conf`	Holistic Evaluation of Text-to-Image Models. NeurIPS 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
2023	A*	`conf`	Kerveros: Efficient and Scalable Cloud Admission Control. OSDI Sultan Mahmud Sajal, Luke Marshall, Beibin Li, Shandan Zhou, Abhisek Pan, Konstantina Mellou, Deepak Narayanan, Timothy Zhu, David Dion, Thomas Moscibroda, Ishai Menache
2023	J	`jnl`	MGit: A Model Versioning and Management System. CoRR Wei Hao, Daniel Mendoza, Rafael da Silva, Deepak Narayanan, Amar Phanishayee
2023	—	`conf`	MegaBlocks: Efficient Sparse Training with Mixture-of-Experts. MLSys Trevor Gale, Deepak Narayanan, Cliff Young, Matei Zaharia
2023	J	`jnl`	Packrat: Automatic Reconfiguration for Latency Minimization in CPU-based DNN Serving. CoRR Ankit Bhardwaj, Amar Phanishayee, Deepak Narayanan, Mihail Tarta, Ryan Stutsman
2022	J	`jnl`	Allocation of fungible resources via a fast, scalable price discovery method. Math. Program. Comput. Akshay Agrawal, Stephen P. Boyd, Deepak Narayanan, Fiodar Kazhamiaka, Matei Zaharia
2022	J	`jnl`	Holistic Evaluation of Language Models. CoRR 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
2022	J	`jnl`	MegaBlocks: Efficient Sparse Training with Mixture-of-Experts. CoRR Trevor Gale, Deepak Narayanan, Cliff Young, Matei Zaharia
2021	J	`jnl`	Allocation of Fungible Resources via a Fast, Scalable Price Discovery Method. CoRR Akshay Agrawal, Stephen P. Boyd, Deepak Narayanan, Fiodar Kazhamiaka, Matei Zaharia
2021	J	`jnl`	Don't Give Up on Large Optimization Problems; POP Them! CoRR Deepak Narayanan, Fiodar Kazhamiaka, Firas Abuzaid, Peter Kraft, Matei Zaharia
2021	J	`jnl`	Efficient Large-Scale Language Model Training on GPU Clusters. CoRR Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, Amar Phanishayee, Matei Zaharia
2021	A	`conf`	Efficient large-scale language model training on GPU clusters using megatron-LM. SC Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, Amar Phanishayee, Matei Zaharia
2021	A*	`conf`	Memory-Efficient Pipeline-Parallel DNN Training. ICML Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, Xie Chen, Matei Zaharia
2021	A*	`conf`	Piper: Multidimensional Planner for DNN Parallelization. NeurIPS Jakub Tarnawski, Deepak Narayanan, Amar Phanishayee
2021	—	`—`	Resource-efficient execution of deep learning computations. Deepak Narayanan
2021	A*	`conf`	Solving Large-Scale Granular Resource Allocation Problems Efficiently with POP. SOSP Deepak Narayanan, Fiodar Kazhamiaka, Firas Abuzaid, Peter Kraft, Akshay Agrawal, Srikanth Kandula, Stephen P. Boyd, Matei Zaharia
2021	J	`jnl`	Solving Large-Scale Granular Resource Allocation Problems Efficiently with POP. CoRR Deepak Narayanan, Fiodar Kazhamiaka, Firas Abuzaid, Peter Kraft, Akshay Agrawal, Srikanth Kandula, Stephen P. Boyd, Matei Zaharia
2020	J	`jnl`	A Demonstration of Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference. Proc. VLDB Endow. Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia
2020	A*	`conf`	Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads. OSDI Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, Matei Zaharia
2020	J	`jnl`	Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads. CoRR Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, Matei Zaharia
2020	—	`conf`	MLPerf Training Benchmark. MLSys 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
2020	J	`jnl`	Memory-Efficient Pipeline-Parallel DNN Training. CoRR Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, Xie Chen, Matei Zaharia
2020	—	`conf`	Offload Annotations: Bringing Heterogeneous Computing to Existing Libraries and Workloads. USENIX ATC Gina Yuan, Shoumik Palkar, Deepak Narayanan, Matei Zaharia
2020	—	`conf`	Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference. MLSys Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia
2019	J	`jnl`	Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark. ACM SIGOPS Oper. Syst. Rev. Cody Coleman, Daniel Kang, Deepak Narayanan, Luigi Nardi, Tian Zhao, Jian Zhang, Peter Bailis, Kunle Olukotun, Christopher Ré, Matei Zaharia
2019	J	`jnl`	MLPerf Training Benchmark. CoRR 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
2019	A*	`conf`	PipeDream: generalized pipeline parallelism for DNN training. SOSP Deepak Narayanan, Aaron Harlap, Amar Phanishayee, Vivek Seshadri, Nikhil R. Devanur, Gregory R. Ganger, Phillip B. Gibbons, Matei Zaharia
2019	J	`jnl`	Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference. CoRR Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia
2018	J	`jnl`	Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark. CoRR Cody Coleman, Daniel Kang, Deepak Narayanan, Luigi Nardi, Tian Zhao, Jian Zhang, Peter Bailis, Kunle Olukotun, Christopher Ré, Matei Zaharia
2018	J	`jnl`	Evaluating End-to-End Optimization for Data Analytics Applications in Weld. Proc. VLDB Endow. 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
2018	J	`jnl`	MacroBase: Prioritizing Attention in Fast Data. ACM Trans. Database Syst. Firas Abuzaid, Peter Bailis, Jialin Ding, Edward Gan, Samuel Madden, Deepak Narayanan, Kexin Rong, Sahaana Suri
2018	J	`jnl`	PipeDream: Fast and Efficient Pipeline Parallel DNN Training. CoRR Aaron Harlap, Deepak Narayanan, Amar Phanishayee, Vivek Seshadri, Nikhil R. Devanur, Gregory R. Ganger, Phillip B. Gibbons
2017	A	`conf`	A Common Runtime for High Performance Data Analysis. CIDR Shoumik Palkar, James Thomas, Anil Shanbhag, Deepak Narayanan, Holger Pirk, Malte Schwarzkopf, Saman P. Amarasinghe, Matei Zaharia
2017	—	`conf`	MacroBase: Prioritizing Attention in Fast Data. SIGMOD Conference Peter Bailis, Edward Gan, Samuel Madden, Deepak Narayanan, Kexin Rong, Sahaana Suri
2017	J	`jnl`	Weld: Rethinking the Interface Between Data-Intensive Applications. CoRR Shoumik Palkar, James Thomas, Deepak Narayanan, Anil Shanbhag, Rahul Palamuttam, Holger Pirk, Malte Schwarzkopf, Saman P. Amarasinghe, Samuel Madden, Matei Zaharia
2016	J	`jnl`	MacroBase: Analytic Monitoring for the Internet of Things. CoRR Peter Bailis, Deepak Narayanan, Samuel Madden