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Over the last few years, Machine Learning/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, Machine Learning is increasingly affecting the design and operation of compute infrastructures. On the other hand, the Machine 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 to discuss three key topics in the context of High Performance Computing and Machine Learning/Deep Learning: parallelization and scaling of ML / DL algorithms, HPC system design and optimization for ML / DL workloads and ML/DL applications on HPC systems.

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

• Unsolved problems in ML / DL on HPC systems
• Scalable Machine Learning / Deep Learning algorithms
• Parallelization techniques
• Libraries for ML / DL
• Tools + workflows for ML / DL on HPC systems
• Optimized HPC system design / setup for efficient ML / DL
• ML 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

-TBA-

[email protected]

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

-TBA-