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High Performance I/O For Large Scale Deep Learning

High Performance I/O For Large Scale Deep Learning

A. Aizman
Gavin Maltby
Thomas Breuel
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Papers citing "High Performance I/O For Large Scale Deep Learning"

5 / 5 papers shown
Title
GlobalGeoTree: A Multi-Granular Vision-Language Dataset for Global Tree Species Classification
GlobalGeoTree: A Multi-Granular Vision-Language Dataset for Global Tree Species Classification
Yang Mu
Zhitong Xiong
Yi Wang
Muhammad Shahzad
Franz Essl
Mark van Kleunen
Xiao Xiang Zhu
VLM
63
0
0
18 May 2025
pCAMP: Performance Comparison of Machine Learning Packages on the Edges
pCAMP: Performance Comparison of Machine Learning Packages on the Edges
Xingzhou Zhang
Yifan Wang
Weisong Shi
28
91
0
05 Jun 2019
Characterizing Deep-Learning I/O Workloads in TensorFlow
Characterizing Deep-Learning I/O Workloads in TensorFlow
Steven W. D. Chien
Stefano Markidis
C. Sishtla
Luís Santos
Pawel Herman
Sai B. Narasimhamurthy
Erwin Laure
36
50
0
06 Oct 2018
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
Chen Sun
Abhinav Shrivastava
Saurabh Singh
Abhinav Gupta
VLM
106
2,378
0
10 Jul 2017
Comparative Study of Deep Learning Software Frameworks
Comparative Study of Deep Learning Software Frameworks
S. Bahrampour
Naveen Ramakrishnan
Lukas Schott
Mohak Shah
43
161
0
19 Nov 2015

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