TY  - CONF
AU  - Comito, C.
AU  - Gutiérrez Hermosillo Muriedas, J. P.
AU  - Götz, M.
AU  - Hagemeier, B.
AU  - Hoppe, F.
AU  - Knechtges, P.
AU  - Krajsek, K.
AU  - Rüttgers, Alexander
AU  - Tarnawa, M.
TI  - Scaling data-intensive analytics with Heat: a Python library for massively-parallel array computing and machine learning
M1  - FZJ-2024-07444
PY  - 2024
AB  - 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.
T2  - Helmholtz AI Conference
CY  - 12 Jun 2024 - 14 Jun 2024, Düsseldorf (Germany)
Y2  - 12 Jun 2024 - 14 Jun 2024
M2  - Düsseldorf, Germany
LB  - PUB:(DE-HGF)24
UR  - https://juser.fz-juelich.de/record/1034687
ER  -