Workshop: Machine Learning on HPC Systems (MLHPCS)

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MLHPCS Workshops

Machine Learning on HPC Systems is workshop series held in conjunction with ISC Supercomputing Conference



Over the last few years, Machine Learning (ML) - and in particular Deep Learning (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. HPC environment and resources on the one hand provide opportunities to attack ML/DL problems not tractable otherwise. On the other hand, the ML/DL community is just getting started to utilize the performance of HPC, leaving out 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 ML/DL methods: 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 2021 online conference

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.

Call for Paper

MLPCS ‘21 has a two stage submission process: submit a 1-2 page extended abstract to contribute with a talk to MLHPCS. Additionally, authors optionally can submit a post-conference paper for publication in LNCS.

Abstract submission

Full paper




Organizing Committee