DLoBD: A Comprehensive Study of D eep L earning o ver B ig D ata Stacks on HPC Clusters

D eep L earning o ver B ig D ata (DLoBD) is an emerging paradigm to mine value from the massive amount of gathered data. Many Deep Learning frameworks, like Caffe, TensorFlow, etc., start running over Big Data stacks, such as Apache Hadoop and Spark. Even though a lot of activities are happening in...

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Veröffentlicht in:	IEEE transactions on multi-scale computing systems 2018-01, Vol.4 (4), p.635
Hauptverfasser:	Lu, Xiaoyi, Shi, Haiyang, Biswas, Rajarshi, Javed, M Haseeb, Panda, Dhabaleswar K
Format:	Artikel
Sprache:	eng
Schlagworte:	Impact analysis Machine learning Stacks
Online-Zugang:	Volltext
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