Physical Sciences and Mathematics Commons^™

Evaluation Of Deep Learning Frameworks Over Different Hpc Architectures, Shayan Shams, Richard Platania, Kisung Lee, Seung Jong Park

Computer Science Faculty Research & Creative Works

Recent advances in deep learning have enabled researchers across many disciplines to uncover new insights about large datasets. Deep neural networks have shown applicability to image, time-series, textual, and other data, all of which are available in a plethora of research fields. However, their computational complexity and large memory overhead requires advanced software and hardware technologies to train neural networks in a reasonable amount of time. To make this possible, there has been an influx in development of deep learning software that aim to leverage advanced hardware resources. In order to better understand the performance implications of deep learning frameworks …