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

Autor: Götz, Markus, Coquelin, Daniel, Debus, Charlotte, Krajsek, Kai, Comito, Claudia, Knechtges, Philipp, Hagemeier, Björn, Tarnawa, Michael, Hanselmann, Simon, Siggel, Martin, Basermann, Achim, Streit, Achim

To cope with the rapid growth in available data, the efficiency of data analysis and machine learning libraries has recently received increased attention. Although great advancements have been made in traditional array-based computations, most are li

Externí odkaz: http://arxiv.org/abs/2007.13552

SLA-based management of software licenses as web service resources in distributed computing infrastructures

Autor: Cacciari, Claudio, Mallmann, Daniel, Zsigri, Csilla, D’Andria, Francesco, Hagemeier, Björn, Rumpl, Angela, Ziegler, Wolfgang, Martrat, Josep

Publikováno v: In Future Generation Computer Systems October 2012 28(8):1340-1349

Analyzing Scientific Big Data with HeAT.

Autor: Comito, Claudia, Götz, Markus, Debus, Charlotte, Coquelin, Daniel, Krajsek, Kai, Knechtges, Philipp, Hagemeier, Björn, Tarnawa, Michael

Publikováno v: ASP Conference Series; 2022, Vol. 532, p89-92, 4p

Developing Exascale Computing at JSC

Autor: Suarez, Estela, Frings, Wolfgang, Krause, Dorian, Di Napoli, Edoardo, Meinke, Jan, Michielsen, Kristel, Mohr, Bernd, Pleiter, Dirk, Strube, Alexandre, Lippert, Thomas, Attig, Norbert, Achilles, Sebastian, De Amicis, Jacopo, Eickermann, Thomas, Gregory, Eric, Hagemeier, Björn, Herten, Andreas, Jitsev, Jenia

Publikováno v: Jülich : Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag, Publication Series of the John von Neumann Institute for Computing (NIC) NIC Series 50, 1-19 (2020).
NIC Symposium 2020
NIC Symposium 2020NIC Symposium 2020, Jülich, Deutschland, 2020-02-27-2020-02-28

The first Exaflop-capable systems will be installed in the USA and China beginning in 2020. Europe intends to have its own machines starting in 2023. It is therefore very timely for computer centres, software providers, and application developers to

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______3364::fac4a89c5c95574ef474eea7a0ca63f2
https://hdl.handle.net/2128/24451

FAIRness in the multi-service data infrastructure of the Tropospheric Ozone Assessment Report (TOAR) and Artificial Intelligence for Air Quality (IntelliAQ) project

Autor: Amirpasha Mozaffari, Schröder, Sabine, Apweiler, Sander, Rajveer Saini, Hagemeier, Björn, Schultz, Martin G.

Publikováno v: RDA 15th Plenary Meeting / Online, RDA-15, Melbourne, Australia, 2020-03-18-2020-03-20
RDA Deutschland Tagung 2020, Berlin, Germany, 2020-02-25-2020-02-27

IntelliAQ is a European project aiming to develop novel approaches for the analysis and synthesis of global air quality data based on deep neural networks. A core element of the project’s strategy is the linkage of several different types of data,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::1eff44b2484ff1dfcb6c2fc78788283e
https://juser.fz-juelich.de/record/890149