This workshop is intended to bring together the Machine Learning (ML), Artificial Intelligence (AI) and High Performance Computing (HPC) communities. In recent years, much progress has been made in Machine Learning and Artificial Intelligence in general. This progress required heavy use of high performance computers and accelerators. Moreover, ML and AI have become a “killer application” for HPC and, consequently, driven much research in this area. These facts point to an important cross-fertilization that this workshop intends to nourish.

We invite researchers and professionals to take part in this workshop to discuss the challenges of Machine Learning, AI and HPC, and share their insights, use cases, tools and best practices.

Proceedings will be published in IEEE Xplore.

Topics of interest include, but are not limited to:

    Distributed and parallel Machine Learning (including deep learning) models
    Large scale Machine Learning applications
    Parallel statistical models
    Large scale data analytics
    Machine learning applied to HPC
    Accelerated Machine Learning
    HPC applied to Machine Learning
    Benchmarking, performance measurements, and analysis of ML models
    Hardware acceleration for ML and AI
    HPC infrastructure and resource management for ML
    Parallel Causal Models
    Cloud-based ML/AI