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1708.05256
Cited By
Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data
17 August 2017
Thorsten Kurth
Jian Zhang
N. Satish
Ioannis Mitliagkas
Evan Racah
M. Patwary
T. Malas
N. Sundaram
W. Bhimji
Mikhail E. Smorkalov
J. Deslippe
Mikhail Shiryaev
Srinivas Sridharan
P. Prabhat
Pradeep Dubey
Re-assign community
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Papers citing
"Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data"
22 / 22 papers shown
Title
SAIH: A Scalable Evaluation Methodology for Understanding AI Performance Trend on HPC Systems
Jiangsu Du
Dongsheng Li
Yingpeng Wen
Jiazhi Jiang
Dan Huang
Xia Liao
Yutong Lu
55
1
0
07 Dec 2022
Identifying the atmospheric drivers of drought and heat using a smoothed deep learning approach
M. Mittermeier
M. Weigert
David Rügamer
38
0
0
09 Nov 2021
Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization
Kartik Ahuja
Ethan Caballero
Dinghuai Zhang
Jean-Christophe Gagnon-Audet
Yoshua Bengio
Ioannis Mitliagkas
Irina Rish
OOD
71
270
0
11 Jun 2021
Crossover-SGD: A gossip-based communication in distributed deep learning for alleviating large mini-batch problem and enhancing scalability
Sangho Yeo
Minho Bae
Minjoong Jeong
Oh-Kyoung Kwon
Sangyoon Oh
50
3
0
30 Dec 2020
AIPerf: Automated machine learning as an AI-HPC benchmark
Zhixiang Ren
Yongheng Liu
Tianhui Shi
Lei Xie
Yue Zhou
Jidong Zhai
Youhui Zhang
Yunquan Zhang
Wenguang Chen
44
23
0
17 Aug 2020
Reducing Data Motion to Accelerate the Training of Deep Neural Networks
Sicong Zhuang
Cristiano Malossi
Marc Casas
27
0
0
05 Apr 2020
A Survey on Distributed Machine Learning
Joost Verbraeken
Matthijs Wolting
Jonathan Katzy
Jeroen Kloppenburg
Tim Verbelen
Jan S. Rellermeyer
OOD
105
714
0
20 Dec 2019
PipeMare: Asynchronous Pipeline Parallel DNN Training
Bowen Yang
Jian Zhang
Jonathan Li
Christopher Ré
Christopher R. Aberger
Christopher De Sa
77
114
0
09 Oct 2019
Accelerating Data Loading in Deep Neural Network Training
Chih-Chieh Yang
Guojing Cong
71
38
0
02 Oct 2019
HPC AI500: A Benchmark Suite for HPC AI Systems
Zihan Jiang
Wanling Gao
Lei Wang
Xingwang Xiong
Yuchen Zhang
...
Yunquan Zhang
Shengzhong Feng
KenLi Li
Weijia Xu
Jianfeng Zhan
ELM
68
40
0
27 Jul 2019
AI Enabling Technologies: A Survey
V. Gadepally
Justin A. Goodwin
J. Kepner
Albert Reuther
Hayley Reynolds
S. Samsi
Jonathan Su
David Martinez
43
25
0
08 May 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
69
49
0
15 Mar 2019
Augment your batch: better training with larger batches
Elad Hoffer
Tal Ben-Nun
Itay Hubara
Niv Giladi
Torsten Hoefler
Daniel Soudry
ODL
118
76
0
27 Jan 2019
CosmoFlow: Using Deep Learning to Learn the Universe at Scale
Amrita Mathuriya
Deborah Bard
P. Mendygral
Lawrence Meadows
James A. Arnemann
...
Nalini Kumar
S. Ho
Michael F. Ringenburg
P. Prabhat
Victor W. Lee
AI4CE
79
126
0
14 Aug 2018
Gradient Energy Matching for Distributed Asynchronous Gradient Descent
Joeri Hermans
Gilles Louppe
46
5
0
22 May 2018
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis
Tal Ben-Nun
Torsten Hoefler
GNN
75
709
0
26 Feb 2018
A Progressive Batching L-BFGS Method for Machine Learning
Raghu Bollapragada
Dheevatsa Mudigere
J. Nocedal
Hao-Jun Michael Shi
P. T. P. Tang
ODL
111
153
0
15 Feb 2018
A Spatial Mapping Algorithm with Applications in Deep Learning-Based Structure Classification
T. Corcoran
R. Zamora-Resendiz
Xinlian Liu
S. Crivelli
3DPC
3DV
40
16
0
07 Feb 2018
On Scale-out Deep Learning Training for Cloud and HPC
Srinivas Sridharan
K. Vaidyanathan
Dhiraj D. Kalamkar
Dipankar Das
Mikhail E. Smorkalov
...
Dheevatsa Mudigere
Naveen Mellempudi
Sasikanth Avancha
Bharat Kaul
Pradeep Dubey
BDL
62
30
0
24 Jan 2018
Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer
Amrita Mathuriya
Thorsten Kurth
V. Rane
M. Mustafa
Lei Shao
D. Bard
P. Prabhat
Victor W. Lee
40
14
0
26 Dec 2017
Scale out for large minibatch SGD: Residual network training on ImageNet-1K with improved accuracy and reduced time to train
V. Codreanu
Damian Podareanu
V. Saletore
63
55
0
12 Nov 2017
Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC
W. Bhimji
S. Farrell
Thorsten Kurth
Michela Paganini
P. Prabhat
Evan Racah
AI4CE
67
48
0
09 Nov 2017
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