Distributed Deep Learning with High Performance Computing for Large-Scale Remote Sensing Data

Wednesday, November 4, 2020
10AM US Eastern Time
Speaker: Dr. -Ing. Gabriele Cavallaro, Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany

Sponsored by GRSS


GRSS Webinar: Distributed Deep Learning with High Performance Computing for Large-Scale Remote Sensing Data

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High Performance Computing (HPC) has recently been attracting more attention in remote sensing applications due to the challenges posed by the increased amount of open data that are acquired daily by Earth Observation programs. The unique parallel computing environments and programming techniques that are integrated in HPC systems are able to solve large-scale problems such as the training of classification algorithms with large amounts of remote sensing data. This webinar will explain how to distribute the training of deep neural networks with parallel implementation techniques on HPC systems that include a large number of Graphics Processing Units. To show that distributed training can drastically reduce the training time and preserve the accuracy performance, the webinar will present recent experimental results performed on the HPC systems at the Jülich Supercomputing Centre.

Register here.


Gabriele Cavallaro received the B.S. and M.S. degrees in telecommunications engineering from the University of Trento, Italy, in 2011 and 2013, respectively, and the Ph.D. degree in electrical and computer engineering from the University of Iceland, Iceland, in 2016. He is currently the deputy head of the “high productivity data processing” research group at the Jülich Supercomputing Centre, Germany. His research interests cover remote sensing data processing with parallel machine learning algorithms that scale on high performance and distributed systems. He was the recipient of the IEEE GRSS Third Prize in the Student Paper Competition of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Since 2019 he gives lectures on scalable machine learning for remote sensing big data at the Institute of Geodesy and Geoinformation, University of Bonn, Germany.

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