%0 Conference Paper
%A Comito, C.
%A Gutiérrez Hermosillo Muriedas, J. P.
%A Götz, M.
%A Hagemeier, B.
%A Hoppe, F.
%A Knechtges, P.
%A Krajsek, K.
%A Rüttgers, Alexander
%A Tarnawa, M.
%T Scaling data-intensive analytics with Heat: a Python library for massively-parallel array computing and machine learning
%M FZJ-2024-07444
%D 2024
%X Handling and analyzing massive data sets is highly important for the vast majority of research communities, but it is also challenging, especially for those communities without a background in HPC. The Helmholtz Analytics Toolkit (Heat) library offers a solution to this problem by providing memory-distributed and hardware-accelerated array manipulation, data analytics, and machine learning algorithms in Python, targeting the usage by non-experts in HPC. In short: Heats objective is to make array computing and machine learning as easy on a CPU/GPU-cluster as it is on a workstation.Our poster provides an overview of Heats design principles, its current features and capabilities, and discusses its role in the ecosystem of distributed array computing and machine learning in Python.
%B Helmholtz AI Conference
%C 12 Jun 2024 - 14 Jun 2024, Düsseldorf (Germany)
Y2 12 Jun 2024 - 14 Jun 2024
M2 Düsseldorf, Germany
%F PUB:(DE-HGF)24
%9 Poster
%U https://juser.fz-juelich.de/record/1034687