Workshop: Machine Learning on HPC Systems (MLHPCS)

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Corona UPDATE

As our hosting confernce ISC canceld this years meeting, we will move on and turn our workshop into an online event within the framework of the planed ISC online event:

Abstract

Over the last few years, Machine Learning (and in particular Deep Learning) (ML / DL) has become an important research topic in the High Performance Computing (HPC) community. Bringing new users and data intensive applications on HPC systems, ML / DL is increasingly affecting the design and operation of compute infrastructures. On the other hand, the ML / DL community is just getting started to utilize the performance of HPC, leaving many opportunities for better parallelization and scalability. The intent of this workshop is to bring together researchers and practitioners to discuss three key topics in the context of High Performance Computing and Machine Learning / Deep Learning: parallelization and scaling of ML / DL algorithms, ML / DL applications on HPC systems, and HPC systems design and optimization for ML / DL workloads.

Date: in conjunction with the ISC 2020 online event

Topics / Scope

The aim of the workshop is to provide a platform for technical discussions and the presentation of work in progress, as well as, unsolved problems, which is complementary to the “Machine Learning Day” in the main conference program.

Paper

MLHPCS paper will be published in the Springer LNCS post Conference Proceedings of the ISC.

Invited Talks

» Deploying machine learning algorithms at Petaflop scale on secure HPC production systems with containers

by David Brayford form LRZ

» Get Together - HPC, Big Data, and Machine Learning

by Sunna Torge form TU Dresden

» Distributed Deep Learning: Challenges & Opportunities

by Peter Labus form Fraunhofer ITWM

Paper Presentations

» SmartPred: Unsupervised Hard Disk Failure Detection

presented by Philipp Rombach form the Institute for Machine Learning and Analytics (IMLA) at Offenburg University, Germany

presented by Emily Herron form the University of Tennessee.

» GOPHER, an HPC framework for large scale graph exploration and inference

by Xavier Teruel form BSC

Contact

info@mlhpcs.org

Organizing Committee