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BagPipe: Accelerating Deep Recommendation Model Training


Feb 24, 2022
Saurabh Agarwal, Ziyi Zhang, Shivaram Venkataraman


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Marius++: Large-Scale Training of Graph Neural Networks on a Single Machine


Feb 04, 2022
Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas, Shivaram Venkataraman


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Doing More by Doing Less: How Structured Partial Backpropagation Improves Deep Learning Clusters


Nov 20, 2021
Adarsh Kumar, Kausik Subramanian, Shivaram Venkataraman, Aditya Akella

* Accepted at DistributedML-2021 

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KAISA: An Adaptive Second-order Optimizer Framework for Deep Neural Networks


Jul 04, 2021
J. Gregory Pauloski, Qi Huang, Lei Huang, Shivaram Venkataraman, Kyle Chard, Ian Foster, Zhao Zhang

* To be published in the proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC21) 

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On the Utility of Gradient Compression in Distributed Training Systems


Mar 03, 2021
Saurabh Agarwal, Hongyi Wang, Shivaram Venkataraman, Dimitris Papailiopoulos


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AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning


Feb 02, 2021
Yuhan Liu, Saurabh Agarwal, Shivaram Venkataraman


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Learning Massive Graph Embeddings on a Single Machine


Jan 20, 2021
Jason Mohoney, Roger Waleffe, Yiheng Xu, Theodoros Rekatsinas, Shivaram Venkataraman

* Under review 

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Accelerating Deep Learning Inference via Learned Caches


Jan 18, 2021
Arjun Balasubramanian, Adarsh Kumar, Yuhan Liu, Han Cao, Shivaram Venkataraman, Aditya Akella


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Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification


Oct 29, 2020
Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, Dimitris Papailiopoulos


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