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EPEEC SIGNED

European joint Effort toward a Highly Productive Programming Environment for Heterogeneous Exascale Computing (EPEEC)

Total Cost €

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EC-Contrib. €

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Partnership

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 EPEEC project word cloud

Explore the words cloud of the EPEEC project. It provides you a very rough idea of what is the project "EPEEC" about.

prediction    pursues    beginning    engagement    code    developers    runtimes    deploy    compiler    incorporate    programming    disciplinary    visualisation    coding    fet    manageable    parallel    companies    expertise    outstanding    adhering    preferred    integrate    shared    flavours    epeec    handle    performance    recognised    turns    composability    distributed    young    extreme    either    builds    participation    domains    leverage    traces    directives    supercomputers    heterogeneous    models    fortran    environment    highest    overarching    serve    runtime    sme    data    memory    profiling    overwhelmingly    start    edge    universities    domain    tools    integral    standardisation    led    exploits    intensive    ready    enhanced    guidelines    components    energy    scientific    language    generator    representative    platforms    upcoming    worldwide    plans    centres    productivity    cutting    porting    significantly    exascale    software    tech    demonstrators    influencing    innovators    public    bodies    automatic    individuals    interoperability    inter    careful    co   

Project "EPEEC" data sheet

The following table provides information about the project.

Coordinator
BARCELONA SUPERCOMPUTING CENTER - CENTRO NACIONAL DE SUPERCOMPUTACION 

Organization address
address: Calle Jordi Girona 31
city: BARCELONA
postcode: 8034
website: www.bsc.es

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Spain [ES]
 Total cost 3˙990˙708 €
 EC max contribution 3˙990˙708 € (100%)
 Programme 1. H2020-EU.1.2.2. (FET Proactive)
 Code Call H2020-FETHPC-2017
 Funding Scheme RIA
 Starting year 2018
 Duration (year-month-day) from 2018-10-01   to  2021-09-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    BARCELONA SUPERCOMPUTING CENTER - CENTRO NACIONAL DE SUPERCOMPUTACION ES (BARCELONA) coordinator 972˙678.00
2    FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V. DE (MUNCHEN) participant 496˙500.00
3    INSTITUT NATIONAL DE RECHERCHE ENINFORMATIQUE ET AUTOMATIQUE FR (LE CHESNAY CEDEX) participant 424˙597.00
4    CENTRE EUROPEEN DE RECHERCHE ET DE FORMATION AVANCEE EN CALCUL SCIENTIFIQUE FR (TOULOUSE) participant 385˙000.00
5    INTERUNIVERSITAIR MICRO-ELECTRONICA CENTRUM BE (LEUVEN) participant 355˙415.00
6    INESC ID - INSTITUTO DE ENGENHARIADE SISTEMAS E COMPUTADORES, INVESTIGACAO E DESENVOLVIMENTO EM LISBOA PT (LISBOA) participant 313˙750.00
7    UPPSALA UNIVERSITET SE (UPPSALA) participant 290˙625.00
8    ETA SCALE AB SE (UPPSALA) participant 269˙375.00
9    APPENTRA SOLUTIONS SL ES (A CORUNA) participant 257˙767.00
10    CINECA CONSORZIO INTERUNIVERSITARIO IT (CASALECCHIO DI RENO BO) participant 225˙000.00

Map

 Project objective

EPEEC’s main goal is to develop and deploy a production-ready parallel programming environment that turns upcoming overwhelmingly-heterogeneous exascale supercomputers into manageable platforms for domain application developers. The consortium will significantly advance and integrate existing state-of-the-art components based on European technology (programming models, runtime systems, and tools) with key features enabling 3 overarching objectives: high coding productivity, high performance, and energy awareness. An automatic generator of compiler directives will provide outstanding coding productivity from the very beginning of the application developing/porting process. Developers will be able to leverage either shared memory or distributed-shared memory programming flavours, and code in their preferred language: C, Fortran, or C\. EPEEC will ensure the composability and interoperability of its programming models and runtimes, which will incorporate specific features to handle data-intensive and extreme-data applications. Enhanced leading-edge performance tools will offer integral profiling, performance prediction, and visualisation of traces. Five applications representative of different relevant scientific domains will serve as part of a strong inter-disciplinary co-design approach and as technology demonstrators. EPEEC exploits results from past FET projects that led to the cutting-edge software components it builds upon, and pursues influencing the most relevant parallel programming standardisation bodies. The consortium is composed of European institutions and individuals with the highest expertise in their field, including not only leading research centres and universities but also SME/start-up companies, all of them recognised as high-tech innovators worldwide. Adhering to the Work Programme’s guidelines, EPEEC features the participation of young and high-potential researchers, and includes careful dissemination, exploitation, and public engagement plans.

 Publications

year authors and title journal last update
List of publications.
2019 Tom Vander Aa, Imen Chakroun, Tom Ashby, Jaak Simm, Adam Arany, Yves Moreau, Thanh Le Van, José Felipe Golib Dzib, Jörg Wegner, Vladimir Chupakhin, Hugo Ceulemans, Roel Wuyts, Wilfried Verachtert
SMURFF: a High-Performance Framework for Matrix Factorization Methods (extended abstract)
published pages: , ISSN: , DOI:
Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019) 2019 2020-04-15
2020 David Gureya, João Neto, Reza Karimi, João Barreto, Pramod Bhatotia, Vivien Quema, Rodrigo Rodrigues, Paolo Romano, Vladimir Vlassov
Bandwidth-Aware Page Placement in NUMA
published pages: , ISSN: , DOI:
Proceedings of the 34th IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2020 2020 2020-04-15
2019 Imen Chakroun, Tom Vander Aa, Tomas J. Ashby
Guidelines for enhancing data locality in selected machine learning algorithms
published pages: 1003-1020, ISSN: 1088-467X, DOI: 10.3233/ida-184287
Intelligent Data Analysis 23/5 2020-04-15

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The information about "EPEEC" are provided by the European Opendata Portal: CORDIS opendata.

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