<span class="var-sub_title">Machine Learning-Aided Numerical Linear Algebra: Convolutional Neural Networks for the Efficient Preconditioner Generation</span> SC18 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

9th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems


Machine Learning-Aided Numerical Linear Algebra: Convolutional Neural Networks for the Efficient Preconditioner Generation

Authors:

Abstract: Markus Götz received his Bachelors and Masters degree in Software Engineering from the University of Potsdam in 2010 and 2014 respectively. Afterwards, he has been with the Research Center Jülich and the University of Iceland, from which Markus obtained his PhD degree in Computational Engineering for his works on parallel data-analysis algorithms on high-performance computing (HPC) systems. Since the beginning of 2018 Markus is with the Steinbuch Centre for Computing (SCC) at the Karlsruhe Institute of Technology (KIT). There, he manages the Helmholtz Analytics Framework project, a german-wide initiative with the aim of developing the data sciences in the Helmholtz Association. His research topics include applied machine learning, scalable data analysis frameworks and parallel algorithms.

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