Machine Learning on HPC Systems

Organizers:

• Juan Jose Durillo (Leibniz Supercomputing Centre)
• Dennis Hoppe (HLRS)
• Janis Keuper (Fraunhofer Institut für Techno- und Wirtschaftsmathematik ITWM)
• Sunna Torge (TU Dresdden)

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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. This comes along with new users and data intensive applications on HPC systems, which increasingly affects the design and operation of compute infrastructures. Bringing new users and data intensive applications on HPC systems, Learning methods are increasingly affecting the design and operation of compute infrastructures. On the other hand, the learning 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 from all communities to discuss three key topics in the context of High Performance Computing and learning methods: parallelization and scaling of ML / DL algorithms, learnig applications on HPC systems, and HPC systems design and optimization for ML / DL workloads.

Workshop website: http://www.mlhpcs.org

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