Characterizing Deep-Learning I/O Workloads in TensorFlow

Characterizing Deep-Learning I/O Workloads in TensorFlow

6 October 2018

Steven W. D. Chien

Stefano Markidis

Sai B. Narasimhamurthy

Papers citing "Characterizing Deep-Learning I/O Workloads in TensorFlow"

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High fusion computers: The IoTs, edges, data centers, and humans-in-the-loop as a computer Wanling Gao Lei Wang Mingyu Chen Jin Xiong Chunjie Luo ... Shaopeng Dai Qian He Hainan Ye Yungang Bao Jianfeng Zhan 11 1 0 18 Nov 2022
Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines Alexander Isenko R. Mayer Jeffrey Jedele Hans-Arno Jacobsen 19 23 0 17 Feb 2022
A Deep Learning-Based Particle-in-Cell Method for Plasma Simulations Xavier Aguilar Stefano Markidis 18 17 0 05 Jul 2021
Clairvoyant Prefetching for Distributed Machine Learning I/O Nikoli Dryden Roman Böhringer Tal Ben-Nun Torsten Hoefler 31 55 0 21 Jan 2021
tf-Darshan: Understanding Fine-grained I/O Performance in Machine Learning Workloads Steven W. D. Chien Artur Podobas Ivy Bo Peng Stefano Markidis 16 11 0 10 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
High Performance I/O For Large Scale Deep Learning A. Aizman Gavin Maltby Thomas Breuel 17 33 0 07 Jan 2020
Efficient Adversarial Training with Transferable Adversarial Examples Haizhong Zheng Ziqi Zhang Juncheng Gu Honglak Lee A. Prakash AAML 24 108 0 27 Dec 2019
Characterizing Deep Learning Training Workloads on Alibaba-PAI Mengdi Wang Chen Meng Guoping Long Chuan Wu Jun Yang Wei Lin Yangqing Jia 22 53 0 14 Oct 2019
Faster Neural Network Training with Data Echoing Dami Choi Alexandre Passos Christopher J. Shallue George E. Dahl 23 48 0 12 Jul 2019