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Synergy: Resource Sensitive DNN Scheduling in Multi-Tenant Clusters


Oct 12, 2021
Jayashree Mohan, Amar Phanishayee, Janardhan Kulkarni, Vijay Chidambaram


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Efficient Large-Scale Language Model Training on GPU Clusters


Apr 09, 2021
Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, Amar Phanishayee, Matei Zaharia


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Analyzing and Mitigating Data Stalls in DNN Training


Jul 14, 2020
Jayashree Mohan, Amar Phanishayee, Ashish Raniwala, Vijay Chidambaram


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Efficient Algorithms for Device Placement of DNN Graph Operators


Jun 29, 2020
Jakub Tarnawski, Amar Phanishayee, Nikhil R. Devanur, Divya Mahajan, Fanny Nina Paravecino


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Memory-Efficient Pipeline-Parallel DNN Training


Jun 16, 2020
Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, Xie Chen, Matei Zaharia


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Daydream: Accurately Estimating the Efficacy of Optimizations for DNN Training


Jun 05, 2020
Hongyu Zhu, Amar Phanishayee, Gennady Pekhimenko


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Blink: Fast and Generic Collectives for Distributed ML


Oct 11, 2019
Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Jorgen Thelin, Nikhil Devanur, Ion Stoica


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The Non-IID Data Quagmire of Decentralized Machine Learning


Oct 01, 2019
Kevin Hsieh, Amar Phanishayee, Onur Mutlu, Phillip B. Gibbons


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Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Training


May 21, 2018
Liang Luo, Jacob Nelson, Luis Ceze, Amar Phanishayee, Arvind Krishnamurthy


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TBD: Benchmarking and Analyzing Deep Neural Network Training


Apr 14, 2018
Hongyu Zhu, Mohamed Akrout, Bojian Zheng, Andrew Pelegris, Amar Phanishayee, Bianca Schroeder, Gennady Pekhimenko


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