An Exascale Library for Numerically Inspired Machine Learning (ExaNIML)

There is a significant gap between algorithms and software in Data Analytics and those in Computational Science and Engineering (CSE) concerning their maturity on High-Performance Computing (HPC) systems. Given the fact that Data Analytics tasks show a rapidly growing share of supercomputer usage, this gap is a serious issue. ExaNIML aims to bridge this gap for a number of important tasks arising, e.g., in a Machine Learning (ML) context: density estimation, and high-dimensional approximation (for example (semi-supervised) classification). To this end, we aim to (1) design and analyze novel algorithms that combine two powerful numerical methods: sparse grids and kernel methods; and to (2) design and implement an HPC library that provides an open-source implementation of these algorithms and supports heterogeneous distributed-memory architectures.     «

There is a significant gap between algorithms and software in Data Analytics and those in Computational Science and Engineering (CSE) concerning their maturity on High-Performance Computing (HPC) systems. Given the fact that Data Analytics tasks show a rapidly growing share of supercomputer usage, this gap is a serious issue. ExaNIML aims to bridge this gap for a number of important tasks arising, e.g., in a Machine Learning (ML) context: density estimation, and high-dimensional approximation (...     »