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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.

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

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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