Wednesday, March 15 • 2:30pm - 3:00pm
Plenary: "Scaling Deep Learning Applications: Theoretical and Practical Limits", Janis Keuper, Fraunhofer ITWM

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Our recent research [1] showed, that distributedly scaling the training of Deep
Neural Networks is a very hard problem which still lacks feasible solutions. In
this talk, we give an introduction to the main theoretical limits prohibiting efficient scalability and current approaches towards applicable solutions.  Also, we discuss many additional practical problems of real world deployments of Deep Learning algorithms to HPC platforms.
Finally, we discuss the consequences and implications of these limitations in the  context of Oil and Gas applications with a focus on algorithms for seismic imaging.

[1] Janis Keuper and Franz-Josef Pfreundt. 2016. Distributed training of deep neural networks: theoretical and practical limits of parallel scalability. In Proceedings of the Workshop on Machine Learning in High Performance Computing Environments (MLHPC '16) at Supercomputing 16.

avatar for Janis Keuper

Janis Keuper

Senior Researcher, Frau
Janis Keuper is a Senior Scientist at ITWM with over 10 years research and application experience in the fields of Machine Learning, Pattern Recognition and Computer Vision. Before joining ITWM in 2012, he was a Group Leader at the Intel Visual Computing Institute (Saarbrücken... Read More →
avatar for Franz-Josef Pfreundt

Franz-Josef Pfreundt

Division Director, Competence Center for HPC
I studied Mathematics, Physics and Computer Science and got a PhD in Mathematical Physics and helped to found the Fraunhofer ITWM in kaiserslautern/ Germany. Today I am leading the Competence Center for HPC. In my group we developed the GPI programming model and the BeeGFS... Read More →

Wednesday March 15, 2017 2:30pm - 3:00pm
Room 103 BRC

Attendees (13)