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.

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.

• Unsolved problems in ML / DL on HPC systems
• Scalable ML / DL algorithms
• Parallelization techniques
• Libraries for ML / DL
• Tools + workflows for ML / DL on HPC systems
• Optimized HPC systems design and setup for efficient ML / DL
• ML / DL applications on HPC Systems

Double blind reviewed workshop paper will be published in the Springer LNCS Conference Proceedings of the ISC.

Please submit your paper via CMT

• 12 pages (including references)
• LNCS Format -> Author Kit

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info@mlhpcs.org

• Juan J. Durillo (LRZ, Munich)
• Dennis Hoppe (HLRS, Stuttgart)
• Jenia Jitsev (JSC, Jülich)
• Janis Keuper (IMLA / Fraunhofer ITWM)
• Sunna Torge (ZIH, Dresden)

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