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001     1034687
005     20250113214946.0
037 _ _ |a FZJ-2024-07444
041 _ _ |a English
100 1 _ |a Comito, C.
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111 2 _ |a Helmholtz AI Conference
|c Düsseldorf
|d 2024-06-12 - 2024-06-14
|w Germany
245 _ _ |a Scaling data-intensive analytics with Heat: a Python library for massively-parallel array computing and machine learning
260 _ _ |c 2024
336 7 _ |a Conference Paper
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336 7 _ |a INPROCEEDINGS
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336 7 _ |a conferenceObject
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336 7 _ |a CONFERENCE_POSTER
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336 7 _ |a Output Types/Conference Poster
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336 7 _ |a Poster
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520 _ _ |a 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.
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536 _ _ |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)
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536 _ _ |a SLNS - SimLab Neuroscience (Helmholtz-SLNS)
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700 1 _ |a Gutiérrez Hermosillo Muriedas, J. P.
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700 1 _ |a Götz, M.
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700 1 _ |a Hagemeier, B.
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700 1 _ |a Hoppe, F.
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700 1 _ |a Knechtges, P.
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700 1 _ |a Krajsek, K.
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700 1 _ |a Rüttgers, Alexander
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700 1 _ |a Tarnawa, M.
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