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Cloud Collectives: Towards Cloud-aware Collectives forML Workloads with Rank Reordering


May 28, 2021
Liang Luo, Jacob Nelson, Arvind Krishnamurthy, Luis Ceze


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AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly


May 22, 2021
Yuchen Jin, Tianyi Zhou, Liangyu Zhao, Yibo Zhu, Chuanxiong Guo, Marco Canini, Arvind Krishnamurthy

* Published as a conference paper at ICLR 2021 

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Scaling Distributed Machine Learning with In-Network Aggregation


Feb 22, 2019
Amedeo Sapio, Marco Canini, Chen-Yu Ho, Jacob Nelson, Panos Kalnis, Changhoon Kim, Arvind Krishnamurthy, Masoud Moshref, Dan R. K. Ports, Peter Richtárik


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ADARES: Adaptive Resource Management for Virtual Machines


Dec 06, 2018
Ignacio Cano, Lequn Chen, Pedro Fonseca, Tianqi Chen, Chern Cheah, Karan Gupta, Ramesh Chandra, Arvind Krishnamurthy


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Learning to Optimize Tensor Programs


Oct 27, 2018
Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

* NIPS 2018 

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TVM: An Automated End-to-End Optimizing Compiler for Deep Learning


Oct 05, 2018
Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

* Significantly improved version, add automated optimization 

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VTA: An Open Hardware-Software Stack for Deep Learning


Jul 11, 2018
Thierry Moreau, Tianqi Chen, Ziheng Jiang, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

* 16 pages, 2 reference pages, 16 figures 

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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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Fast Video Classification via Adaptive Cascading of Deep Models


Jul 02, 2017
Haichen Shen, Seungyeop Han, Matthai Philipose, Arvind Krishnamurthy

* Accepted at IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 

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