TY  - CONF
AU  - Comito, Claudia
AU  - Götz, Markus
AU  - Gutiérrez Hermosillo Muriedas, Juan Pedro
AU  - Hagemeier, Björn
AU  - Hoppe, Fabian
AU  - Knechtges, Philipp
AU  - Krajsek, Kai
AU  - Rüttgers, Alexander
AU  - Streit, Achim
AU  - Tarnawa, Michael
TI  - Accelerating massive data processing in Python with Heat
M1  - FZJ-2023-05810
PY  - 2023
AB  - Heat [1, 2] is an open-source Python library designed to address the challenges of working with massive data sets and harnessing the power of machine learning across disciplines. Developed collaboratively by within the Helmholtz Association (FZJ, KIT, and DLR), Heat offers cutting-edge capabilities for high-performance data analytics, machine learning, and deep learning.Heat provides a Numpy-like API that simplifies the development of scalable, GPU-accelerated applications. What sets Heat apart is its underlying data-parallelism, implemented on top of MPI, which significantly enhances efficiency and performance of data processing compared to traditional task-parallel frameworks.By exploring practical use cases in space science (materials engineering, atmospheric modeling, anomaly detection) and its potential as a backend for diverse data processing pipelines, we will illustrate how Heat can accelerate AI research and applications.[1] Götz, M., Debus, C., Coquelin, et al.: "HeAT - a Distributed and GPU-accelerated Tensor Framework for Data Analytics" [2] https://github.com/helmholtz-analytics/heat
T2  - Artificial Intelligence Symposium on Theory, Application and Research 2023
CY  - 27 Sep 2023 - 28 Sep 2023, ESOC, Darmstadt (Germany)
Y2  - 27 Sep 2023 - 28 Sep 2023
M2  - ESOC, Darmstadt, Germany
LB  - PUB:(DE-HGF)24
UR  - https://juser.fz-juelich.de/record/1019995
ER  -