Integrated Model, Batch and Domain Parallelism in Training Neural Networks

12 December 2017

Papers citing "Integrated Model, Batch and Domain Parallelism in Training Neural Networks"

21 / 21 papers shown

Title
Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Training Jared Fernandez Luca Wehrstedt Leonid Shamis Mostafa Elhoushi Kalyan Saladi Yonatan Bisk Emma Strubell Jacob Kahn 221 3 0 20 Nov 2024
Neural Network Methods for Radiation Detectors and Imaging S. Lin S. Ning H. Zhu T. Zhou C. L. Morris S. Clayton M. Cherukara R. T. Chen Z. Wang AI4CE 32 5 0 09 Nov 2023
LOFT: Finding Lottery Tickets through Filter-wise Training Qihan Wang Chen Dun Fangshuo Liao C. Jermaine Anastasios Kyrillidis 23 3 0 28 Oct 2022
OLLA: Optimizing the Lifetime and Location of Arrays to Reduce the Memory Usage of Neural Networks Benoit Steiner Mostafa Elhoushi Jacob Kahn James Hegarty 29 8 0 24 Oct 2022
Model-Parallel Model Selection for Deep Learning Systems Kabir Nagrecha 37 16 0 14 Jul 2021
ResIST: Layer-Wise Decomposition of ResNets for Distributed Training Chen Dun Cameron R. Wolfe C. Jermaine Anastasios Kyrillidis 16 21 0 02 Jul 2021
An Oracle for Guiding Large-Scale Model/Hybrid Parallel Training of Convolutional Neural Networks A. Kahira Truong Thao Nguyen L. Bautista-Gomez Ryousei Takano Rosa M. Badia M. Wahib 15 9 0 19 Apr 2021
Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM Deepak Narayanan M. Shoeybi Jared Casper P. LeGresley M. Patwary ... Prethvi Kashinkunti J. Bernauer Bryan Catanzaro Amar Phanishayee Matei A. Zaharia MoE 37 646 0 09 Apr 2021
GIST: Distributed Training for Large-Scale Graph Convolutional Networks Cameron R. Wolfe Jingkang Yang Arindam Chowdhury Chen Dun Artun Bayer Santiago Segarra Anastasios Kyrillidis BDL GNN LRM 51 9 0 20 Feb 2021
Integrating Deep Learning in Domain Sciences at Exascale Rick Archibald E. Chow E. DÁzevedo Jack J. Dongarra M. Eisenbach ... Florent Lopez Daniel Nichols S. Tomov Kwai Wong Junqi Yin PINN 23 5 0 23 Nov 2020
Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA M. Wahib Haoyu Zhang Truong Thao Nguyen Aleksandr Drozd Jens Domke Lingqi Zhang Ryousei Takano Satoshi Matsuoka OODD 34 23 0 26 Aug 2020
The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism Yosuke Oyama N. Maruyama Nikoli Dryden Erin McCarthy P. Harrington J. Balewski Satoshi Matsuoka Peter Nugent B. Van Essen 3DV AI4CE 32 37 0 25 Jul 2020
ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation Tianchen Wang Xiaowei Xu Jinjun Xiong Qianjun Jia Haiyun Yuan Meiping Huang Jian Zhuang Yiyu Shi 14 21 0 18 Jul 2020
Data Movement Is All You Need: A Case Study on Optimizing Transformers A. Ivanov Nikoli Dryden Tal Ben-Nun Shigang Li Torsten Hoefler 36 131 0 30 Jun 2020
Reducing Communication in Graph Neural Network Training Alok Tripathy Katherine Yelick A. Buluç GNN 30 104 0 07 May 2020
Optimizing Multi-GPU Parallelization Strategies for Deep Learning Training Saptadeep Pal Eiman Ebrahimi A. Zulfiqar Yaosheng Fu Victor Zhang Szymon Migacz D. Nellans Puneet Gupta 34 55 0 30 Jul 2019
Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism Nikoli Dryden N. Maruyama Tom Benson Tim Moon M. Snir B. Van Essen 26 49 0 15 Mar 2019
Parameter Re-Initialization through Cyclical Batch Size Schedules Norman Mu Z. Yao A. Gholami Kurt Keutzer Michael W. Mahoney ODL 30 8 0 04 Dec 2018
SqueezeNext: Hardware-Aware Neural Network Design A. Gholami K. Kwon Bichen Wu Zizheng Tai Xiangyu Yue Peter H. Jin Sicheng Zhao Kurt Keutzer 22 295 0 23 Mar 2018
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis Tal Ben-Nun Torsten Hoefler GNN 33 702 0 26 Feb 2018
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 308 2,890 0 15 Sep 2016