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DTSTAMP:20190719T085743Z
LOCATION:HG F 1
DTSTART;TZID=Europe/Stockholm:20190612T130000
DTEND;TZID=Europe/Stockholm:20190612T133000
UID:submissions.pasc-conference.org_PASC19_sess138_msa187@linklings.com
SUMMARY:HeAT - A New Versatile Distributed-Parallel Machine Learning Toolk
 it
DESCRIPTION:Minisymposium\nComputer Science and Applied Mathematics, Chemi
 stry and Materials, Physics, Engineering\n\nHeAT - A New Versatile Distrib
 uted-Parallel Machine Learning Toolkit\n\nKnechtges\n\nIn this talk we wil
 l present the Helmholtz Analytics Toolkit (HeAT), a scientific big data an
 alytics library for HPC systems. Big data analytics, and machine learning 
 in particular, has gained a lot of traction recently with applications ran
 ging from neuroscience to aeronautics. At the heart of many of these metho
 ds lies the idea to model even discrete decision processes with continuous
  functions, whose programmatic handling of course necessitates numerical l
 inear algebra, a prime subject of HPC. However, many classical HPC algorit
 hms are designed with sparsity in mind, a property most often inherited fr
 om the locality of a physical description. Since such a locality is missin
 g a priori in most big data scenarios, it poses new challenges on the effi
 cient algorithm design. We will discuss our current efforts to make such a
 lgorithms available to the broader public in the HeAT framework.
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