Session

DescriptionResearch and industry invest considerable efforts into using high performance computing to advance machine learning. In this mini-symposium, we propose to explore the confluence of HPC and Machine learning in the opposite direction, and ask: How can machine learning advance high performance computing? The expanding landscape of complex and heterogeneous hardware, programming paradigms and tools, means more expertise is needed to develop optimal programs for supercomputing systems and make them portable. Simultaneously though, advances in machine learning and natural language processing, as well as the large amounts of code made available by open-source repositories present opportunities in tackling problems previously unsolved using machine learning. Four talks will present methods that use machine learning in current HPC systems or applications for: performance modelling and optimization; autotuning; automatic code analysis; and optimized compilation. By showcasing these research topics and bringing together researchers from both the HPC and the ML communities, we intend to raise awareness, in the HPC community, of solutions machine learning offer to rising HPC problems as well as to create a platform for exchange among actors at the interface between HPC and ML.