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With widespread advances in machine learning, a number of large enterprises are beginning to incorporate machine learning models across a number of products.
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An Analysis of Traces from a Production MapReduce Cluster
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Recurrent Neural Network Based Language Model
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The Hadoop Distributed File System
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Delay Scheduling: A Simple Technique for Achieving Locality and Fairness in Cluster Scheduling
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Scarlett: Coping with Skewed Content Popularity in Mapreduce Clusters
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ImageNet Classification with Deep Convolutional Neural Networks
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Jeffrey Dean and Luiz André Barroso · 2013
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Sparrow: Distributed, Low Latency Scheduling
Kay Ousterhout, Patrick Wendell, Matei Zaharia, and Ion Stoica · 2013
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Omega: Flexible, Scalable Schedulers for Large Compute Clusters
Malte Schwarzkopf, Andy Konwinski, Michael Abd-El-Malek, and John Wilkes · 2013
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Apache Hadoop YARN: Yet Another Resource Negotiator
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GRASS: Trimming Stragglers in Approximation Analytics
Ganesh Ananthanarayanan, Michael Chien-Chun Hung, Xiaoqi Ren, Ion Stoica, Adam Wierman, and Minlan Yu · 2014
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Apollo: Scalable and Coordinated Scheduling for Cloud-scale Computing
Eric Boutin, Jaliya Ekanayake, Wei Lin, Bing Shi, Jingren Zhou, Zhengping Qian, Ming Wu, and Lidong Zhou · 2014
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Caffe: Convolutional Architecture for Fast Feature Embedding
iOLAP: Managing Uncertainty for Efficient Incremental OLAP
Kai Zeng, Sameer Agarwal, and Ion Stoica · 2016
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Deep Learning for Siri’s Voice · 2017
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Using Deep Learning to Create Professional-Level Photographs · 2017
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Prophet: Precise QoS Prediction on Non-Preemptive Accelerators to Improve Utilization in Warehouse-Scale Computers
Quan Chen, Hailong Yang, Minyi Guo, Ram Srivatsa Kannan, Jason Mars, and Lingjia Tang · 2017
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Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
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Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
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Scaling Distributed Machine Learning with the Parameter Server
Mu Li, David G Andersen, Jun Woo Park, Alexander J Smola, Amr Ahmed, Vanja Josifovski, James Long, Eugene J Shekita, and Bor-Yiing Su · 2014
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The Power of Choice in Data-aware Cluster Scheduling
Shivaram Venkataraman, Aurojit Panda, Ganesh Ananthanarayanan, Michael J. Franklin, and Ion Stoica · 2014
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MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang · 2015
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Going Deeper With Convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Large-scale Cluster Management at Google with Borg
Abhishek Verma, Luis Pedrosa, Madhukar Korupolu, David Oppenheimer, Eric Tune, and John Wilkes · 2015
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TensorFlow: A System for Large-Scale Machine Learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
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Avner May, Alireza Bagheri Garakani, Zhiyun Lu, Dong Guo, Kuan Liu, Aurélien Bellet, Linxi Fan, Michael Collins, Daniel Hsu, Brian Kingsbury, et al · 2017
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Attention Is All You Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Full Virtualization for GPUs Reconsidered
Hangchen Yu and Christopher J. Rossbach · 2017
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SLAQ: Quality-driven Scheduling for Distributed Machine Learning
Haoyu Zhang, Logan Stafman, Andrew Or, and Michael J. Freedman · 2017
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Multi-tenant GPU Clusters for Deep Learning Workloads: Analysis and Implications
Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, Junjie Qian, Wencong Xiao, and Fan Yang · 2018
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Open Platform for AI, 2018 · 2018
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Optimus: An Efficient Dynamic Resource Scheduler for Deep Learning Clusters
Yanghua Peng, Yixin Bao, Yangrui Chen, Chuan Wu, and Chuanxiong Guo · 2018
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https://pytorch.org/
PyTorch, 2018 · 2018
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Superneurons: Dynamic GPU Memory Management for Training Deep Neural Networks
Linnan Wang, Jinmian Ye, Yiyang Zhao, Wei Wu, Ang Li, Shuaiwen Leon Song, Zenglin Xu, and Tim Kraska · 2018
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Gandiva: Introspective Cluster Scheduling for Deep Learning
Wencong Xiao, Romil Bhardwaj, Ramachandran Ramjee, Muthian Sivathanu, Nipun Kwatra, Zhenhua Han, Pratyush Patel, Xuan Peng, Hanyu Zhao, Quanlu Zhang, Fan Yang, Lidong Zhou · 2018
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Tiresias: A GPU Cluster Manager for Distributed Deep Learning
Juncheng Gu, Kang G. Chowdhury, Mosharaf abd Shin, Yibo Zhu, Myeongjae Jeon, Junjie Qian, Hongqiang Liu, and Chuanxiong Guo · 2019
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https://github.com/msr-fiddle/philly-traces
DNN training workloads on Microsoft’s internal Philly clusters, 2019 · 2019
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https://hadoop.apache.org/docs/r3.2.0/hadoop-yarn/hadoop-yarn-applications/hadoop-yarn-submarine/
Apache Hadoop 3.2.0 Submarine, 2019 · 2019
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