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Large-scale machine learning in cancer and brain research: new applications that will drive future supercomputing systems

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45-46 2015-16 SORS BoA Large-Scale Machine Learning.pdf (141,3Kb)
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Stevens, Rick
Document typeConference report
Defense date2016-09-10
PublisherBarcelona Supercomputing Center
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
In this talk I’ll discuss the DOE/NCI cancer research co-design projects that are a key part of the Presidential Moonshot Cancer Initiative and the brain connectome project at the heart of the National Brain Observatory concept being developed at Argonne. These two projects are aimed at major problems in cancer and brain research and are emerging as major Exascale computing drivers that require the integration of large-scale machine learning, data analytics and simulation. I will also discuss our Argonne computing roadmap including the Athena, Theta and Aurora supercomputers and new directions we are investigating for hardware acceleration of deep learning applications in future large-scale platforms.
CitationStevens, Rick. Large-scale machine learning in cancer and brain research: new applications that will drive future supercomputing systems. A: 2nd Severo Ochoa Research Seminar Lectures at BSC, Barcelona, 2015-2016. "Book of abstracts". Barcelona: Barcelona Supercomputing Center, 2016, p. 44-46. 
URIhttp://hdl.handle.net/2117/96661
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