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HeAT: The Helmholtz Analytics ToolKit

Debus, Charlotte and Knechtges, Philipp and Siggel, Martin (2019) HeAT: The Helmholtz Analytics ToolKit. Wissensaustausch Workshop Maschinelles Lernen WAW ML5, 2019-06-06 - 2019-06-07, Köln-Porz, Deutschland.

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HeAT is a distributed tensor framework for high performance data analytics. The goal of HeAT is to fill the gap between machine learning libraries that have a strong focus on exploiting GPUs for performance, and traditional, distributed high-performance computing (HPC). The basic idea is to provide a generic, distributed tensor library with machine learning methods based on it.

Item URL in elib:https://elib.dlr.de/132358/
Document Type:Conference or Workshop Item (Poster)
Additional Information:Teil des "Helmholtz Analytics Frameworks" (Projektnr. ZT-I-0003)
Title:HeAT: The Helmholtz Analytics ToolKit
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Debus, CharlotteUNSPECIFIEDhttps://orcid.org/0000-0002-7156-2022UNSPECIFIED
Knechtges, PhilippUNSPECIFIEDhttps://orcid.org/0000-0002-4849-0593UNSPECIFIED
Siggel, MartinUNSPECIFIEDhttps://orcid.org/0000-0002-3952-4659UNSPECIFIED
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Keywords:Software Framework, Machine Learning
Event Title:Wissensaustausch Workshop Maschinelles Lernen WAW ML5
Event Location:Köln-Porz, Deutschland
Event Type:Workshop
Event Start Date:6 June 2019
Event End Date:7 June 2019
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:other
DLR - Research area:Raumfahrt
DLR - Program:R - no assignment
DLR - Research theme (Project):R - no assignment
Location: Köln-Porz
Institutes and Institutions:Institut of Simulation and Software Technology > High Performance Computing
Deposited By: Debus, Charlotte
Deposited On:09 Dec 2019 11:43
Last Modified:24 Apr 2024 20:35

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