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DEEP-EST

DEEP - Extreme Scale Technologies

CoordinatorEXTOLL GMBH ; STICHTING NEDERLANDSE WETENSCHAPPELIJK ONDERZOEK INSTITUTEN ; AURORA SRL ; MEGWARE COMPUTER VERTRIEB UND SERVICE GMBH ; Bavarian Academy of Sciences and Humanities ; BARCELONA SUPERCOMPUTING CENTER - CENTRO NACIONAL DE SUPERCOMPUTACION ; Heidelberg University ; HASKOLI ISLANDS ; ASSOCIATION "NATIONAL CENTRE FOR SUPERCOMPUTING APPLICATIONS ; NORGES MILJO-OG BIOVITENSKAPLIGE UNIVERSITET ; INTEL DEUTSCHLAND GMBH ; STICHTING ASTRON, NETHERLANDS INSTITUTE FOR RADIO ASTRONOMY ; Edinburgh Napier University ; FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V. ; CERN ; Forschungszentrum Jülich ; KU Leuven
Grant period2017-07-01 - 2021-03-31
Funding bodyEuropean Union
Call numberH2020-FETHPC-2016
Grant number754304
IdentifierG:(EU-Grant)754304

Note: The DEEP-EST (“DEEP - Extreme Scale Technologies”) project will create a first incarnation of the Modular Supercomputer Architecture (MSA) and demonstrate its benefits. In the spirit of the DEEP and DEEP-ER projects, the MSA integrates compute modules with different performance characteristics into a single heterogeneous system. Each module is a parallel, clustered system of potentially large size. A federated network connects the module-specific interconnects. MSA brings substantial benefits for heterogeneous applications/workflows: each part can be run on an exactly matching system, improving time to solution and energy use. This is ideal for supercomputer centres running heterogeneous application mixes (higher throughput and energy efficiency). It also offers valuable flexibility to the compute providers, allowing the set of modules and their respective size to be tailored to actual usage. The DEEP-EST prototype will include three modules: general purpose Cluster Module and Extreme Scale Booster supporting the full range of HPC applications, and Data Analytics Module specifically designed for high-performance data analytics (HPDA) workloads. Proven programming models and APIs from HPC (combining MPI and OmpSs) and HPDA will be extended and combined with a significantly enhanced resource management and scheduling system to enable straightforward use of the new architecture and achieve highest system utilisation and performance. Scalability projections will be given up to the Exascale performance class. The DEEP-EST prototype will be defined in close co-design between applications, system software and system component architects. Its implementation will employ European integration, network and software technologies. Six ambitious and highly relevant European applications from HPC and HPDA domains will drive the co-design, serving to evaluate the DEEP EST prototype and demonstrate the benefits of its innovative Modular Supercomputer Architecture.
     

Recent Publications

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NEST user-level documentation: Why, how and what's next?
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http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Contribution to a conference proceedings/Contribution to a book  ;  ;  ;  ;  ;  ;
Portability of Multiphysics Applications on Heterogeneous Modular Supercomputers
Parallel Processing and Applied Mathematics
International Conference on Parallel Processing and Applied Mathematics, PPAM 2024, OstravaOstrava, Czech Republic, 8 Sep 2024 - 11 Sep 20242024-09-082024-09-11 Cham : Springer Nature Switzerland, Lecture Notes in Computer Science 15581, 47 - 62 () [10.1007/978-3-031-85703-4_4] OpenAccess  Download fulltext Files  Download fulltextFulltext BibTeX | EndNote: XML, Text | RIS

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Journal Article  ;  ;
The DEEP-SEA project: a software stack for heterogeneous and modular supercomputers
PARS-Mitteilungen 36, 7-25 ()  Download fulltext Files BibTeX | EndNote: XML, Text | RIS

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Poster (After Call)  ;  ;  ;  ;  ;
Routing brain traffic through the von Neumann bottleneck: Optimization strategies for efficient spike delivery in large-scale network simulations on conventional hardware
International conference on neuromorphic, natural and physical computing, HanoverHanover, Germany, 25 Oct 2023 - 27 Oct 20232023-10-252023-10-27 BibTeX | EndNote: XML, Text | RIS

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HBP scientists propose guidelines for describing network connectivity
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http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Book/Dissertation / PhD Thesis
Design and Evaluation of Parallel and Scalable Machine Learning Research in Biomedical Modelling Applications
Reykjavík, Iceland xxiv, 108 () [10.34734/FZJ-2023-02488] = Dissertation, Háskóli Íslands, 2023 OpenAccess  Download fulltext Files  Download fulltextFulltext BibTeX | EndNote: XML, Text | RIS

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Developing an Artificial Intelligence-Based Representation of a Virtual Patient Model for Real-Time Diagnosis of Acute Respiratory Distress Syndrome
Diagnostics 13(12), 2098 () [10.3390/diagnostics13122098] special issue: "AI-Driven Intelligent Health Care Diagnostic Solutions: A Machine Learning Approach" OpenAccess  Download fulltext Files BibTeX | EndNote: XML, Text | RIS

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Poster (After Call)  ;  ;  ;  ;  ;  ;  ;  ;  ;
Concepts for reproducible network connectivity
Human Brain Project Summit 2023, MarseilleMarseille, France, 27 Mar 2023 - 31 Mar 20232023-03-272023-03-31 () DBCoverage   Download fulltextFulltext BibTeX | EndNote: XML, Text | RIS

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Talk (non-conference) (Invited)  ;  ;  ;  ;
Brain Matters #15 - Connectivity concepts in neuronal network modeling
Seminar, onlineonline, 14 Feb 20232023-02-14   Download fulltextFulltext BibTeX | EndNote: XML, Text | RIS

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Conference Presentation (Invited)
On concepts, correctness, and performance of network simulations
INCF Neuroinformatics Assembly 2022, virtualvirtual, virtual, 13 Sep 2022 - 13 Sep 20222022-09-132022-09-13 BibTeX | EndNote: XML, Text | RIS

All known publications ...
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 Datensatz erzeugt am 2017-06-12, letzte Änderung am 2023-02-07
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