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HeAT - a Distributed and GPU-accelerated Tensor Framework for Data Analytics

Goetz, Markus and Debus, Charlotte and Coquelin, Daniel and Krajsek, Kai and Comito, Claudia and Knechtges, Philipp and Hagemeier, Björn and Tarnawa, Michael and Hanselmann, Simon and Siggel, Martin and Basermann, Achim and Streit, Achim (2020) HeAT - a Distributed and GPU-accelerated Tensor Framework for Data Analytics. In: 6th IEEE International Conference on Multimedia Big Data, BigMM 2020. IEEE International Conference on Big Data, 10. - 13. Dez. 2020, Online. ISBN 978-172819325-0.

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Abstract

To cope with the rapid growth in available data, theefficiency of data analysis and machine learning libraries has re-cently received increased attention. Although great advancementshave been made in traditional array-based computations, mostare limited by the resources available on a single computationnode. Consequently, novel approaches must be made to exploitdistributed resources, e.g. distributed memory architectures. Tothis end, we introduce HeAT, an array-based numerical pro-gramming framework for large-scale parallel processing withan easy-to-use NumPy-like API. HeAT utilizes PyTorch as anode-local eager execution engine and distributes the workloadon arbitrarily large high-performance computing systems viaMPI. It provides both low-level array computations, as wellasassorted higher-level algorithms. With HeAT, it is possible for aNumPy user to take full advantage of their available resources,significantly lowering the barrier to distributed data analysis.When compared to similar frameworks, HeAT achieves speedupsof up to two orders of magnitude.

Item URL in elib:https://elib.dlr.de/139096/
Document Type:Conference or Workshop Item (Speech)
Title:HeAT - a Distributed and GPU-accelerated Tensor Framework for Data Analytics
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Goetz, Markusmarkus.goetz (at) kit.eduUNSPECIFIED
Debus, CharlotteCharlotte.Debus (at) dlr.deUNSPECIFIED
Coquelin, Danieldaniel.coquelin (at) kit.eduUNSPECIFIED
Krajsek, Kaik.krajsek (at) fz-juelich.dehttps://orcid.org/0000-0003-3417-161X
Comito, Claudiac.comito (at) fz-juelich.deUNSPECIFIED
Knechtges, PhilippPhilipp.Knechtges (at) dlr.dehttps://orcid.org/0000-0002-4849-0593
Hagemeier, Björnb.hagemeier (at) fz-juelich.dehttps://orcid.org/0000-0003-1528-0933
Tarnawa, Michaelm.tarnawa (at) fz-juelich.deUNSPECIFIED
Hanselmann, Simonsimon.hanselmann (at) kit.eduUNSPECIFIED
Siggel, Martinmartin.siggel (at) dlr.dehttps://orcid.org/0000-0002-3952-4659
Basermann, AchimAchim.Basermann (at) dlr.dehttps://orcid.org/0000-0003-3637-3231
Streit, Achimachim.streit (at) kit.eduUNSPECIFIED
Date:November 2020
Journal or Publication Title:6th IEEE International Conference on Multimedia Big Data, BigMM 2020
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
ISBN:978-172819325-0
Status:Accepted
Keywords:HeAT, Tensor Framework, High-performanceComputing, PyTorch, NumPy, Message Passing Interface, GPU,Big Data Analytics, Machine Learning, Dask, Model Parallelism,Parallel Application Frameworks
Event Title:IEEE International Conference on Big Data
Event Location:Online
Event Type:international Conference
Event Dates:10. - 13. Dez. 2020
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - Vorhaben SISTEC (old)
Location: Köln-Porz
Institutes and Institutions:Institut of Simulation and Software Technology > High Performance Computing
Institute of Flight Systems > Simulation Technology
Deposited By: Knechtges, Philipp
Deposited On:07 Dec 2020 10:01
Last Modified:17 Dec 2020 10:22

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