Scaling Deep Learning on GPU and Knights Landing clusters

Scaling Deep Learning on GPU and Knights Landing clusters

International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2017

9 August 2017

Yang You

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Papers citing "Scaling Deep Learning on GPU and Knights Landing clusters"

18 / 18 papers shown

Title
Estudio de la eficiencia en la escalabilidad de GPUs para el entrenamiento de Inteligencia Artificial David Cortes Carlos Juiz Belen Bermejo 60 0 0 03 Sep 2025
DeAR: Accelerating Distributed Deep Learning with Fine-Grained All-Reduce PipeliningIEEE International Conference on Distributed Computing Systems (ICDCS), 2023 Lin Zhang Shaoshuai Shi Xiaowen Chu Wei Wang Yue Liu Chengjian Liu 157 17 0 24 Feb 2023
Towards Efficient Communications in Federated Learning: A Contemporary SurveyJournal of the Franklin Institute (JFI), 2022 Zihao Zhao Yuzhu Mao Yang Liu Linqi Song Ouyang Ye Xinlei Chen Wenbo Ding FedML 288 69 0 02 Aug 2022
Reconfigurable Cyber-Physical System for Lifestyle Video-Monitoring via Deep Learning Daniel Deniz Francisco Barranco J. Isern Eduardo Ros 76 9 0 07 Oct 2020
Reducing Data Motion to Accelerate the Training of Deep Neural Networks Sicong Zhuang Cristiano Malossi Marc Casas 71 0 0 05 Apr 2020
Communication-Efficient Distributed Deep Learning: A Comprehensive Survey Zhenheng Tang Shaoshuai Shi Wei Wang Yue Liu Xiaowen Chu 218 54 0 10 Mar 2020
Communication Contention Aware Scheduling of Multiple Deep Learning Training Jobs Qiang-qiang Wang Shaoshuai Shi Canhui Wang Xiaowen Chu 190 14 0 24 Feb 2020
Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning ApproachIEEE International Conference on Distributed Computing Systems (ICDCS), 2020 Pengchao Han Maroun Touma K. Leung FedML 250 212 0 14 Jan 2020
A Survey on Distributed Machine LearningACM Computing Surveys (ACM CSUR), 2019 Joost Verbraeken Matthijs Wolting Jonathan Katzy Jeroen Kloppenburg Tim Verbelen Jan S. Rellermeyer OOD 242 809 0 20 Dec 2019
MG-WFBP: Merging Gradients Wisely for Efficient Communication in Distributed Deep LearningIEEE Transactions on Parallel and Distributed Systems (TPDS), 2019 Shaoshuai Shi Xiaowen Chu Bo Li FedML 138 30 0 18 Dec 2019
Layer-wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence GuaranteesEuropean Conference on Artificial Intelligence (ECAI), 2019 Shaoshuai Shi Zhenheng Tang Qiang-qiang Wang Kaiyong Zhao Xiaowen Chu 229 27 0 20 Nov 2019
AI Enabling Technologies: A Survey V. Gadepally Justin A. Goodwin J. Kepner Albert Reuther Hayley Reynolds S. Samsi Jonathan Su David Martinez 93 26 0 08 May 2019
On Linear Learning with Manycore ProcessorsInternational Conference on High Performance Computing (HiPC), 2019 Eliza Wszola Celestine Mendler-Dünner Martin Jaggi Markus Püschel 371 1 0 02 May 2019
MG-WFBP: Efficient Data Communication for Distributed Synchronous SGD Algorithms Shaoshuai Shi Xiaowen Chu Bo Li FedML 154 99 0 27 Nov 2018
Runtime Concurrency Control and Operation Scheduling for High Performance Neural Network Training Jiawen Liu Dong Li Gokcen Kestor Jeffrey S. Vetter 104 10 0 21 Oct 2018
GeneSys: Enabling Continuous Learning through Neural Network Evolution in Hardware A. Samajdar Parth Mannan K. Garg T. Krishna 187 21 0 03 Aug 2018
GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent J. Daily Abhinav Vishnu Charles Siegel T. Warfel Vinay C. Amatya 145 99 0 15 Mar 2018
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency AnalysisACM Computing Surveys (CSUR), 2018 Tal Ben-Nun Torsten Hoefler GNN 268 760 0 26 Feb 2018