MLHPCS 2021: Call for Papers

12.04.2021 // SCADS

The 2nd Workshop on Machine Learning on HPC Systems (MLHPCS) is looking for your ideas! MLHPCS ‘21 has a two stage submission process. First, you 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!

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. 

Important Dates

→  Extended Abstracts deadline: May 14th

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.

Topics of MLHPCS 2021

  • 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

MLHPCS will be held as a live online workshop within the ISC conference on 02/07/2021. Our team members Dr. Peter Winkler and Norman Koch will contribute a talk on Hyper-parameter optimization on HPC – a comparative study. Post-Conference papers will be published in Springer LNCS. Find out more about the workshop on Github.

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