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ASPIDE SIGNED

ASPIDE: exAScale ProgramIng models for extreme Data procEssing

Total Cost €

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EC-Contrib. €

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Partnership

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Project "ASPIDE" data sheet

The following table provides information about the project.

Coordinator
UNIVERSIDAD CARLOS III DE MADRID 

Organization address
address: CALLE MADRID 126
city: GETAFE (MADRID)
postcode: 28903
website: http://www.uc3m.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 2˙464˙093 €
 EC max contribution 2˙464˙093 € (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 2018-06-15   to  2020-12-14

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSIDAD CARLOS III DE MADRID ES (GETAFE (MADRID)) coordinator 338˙600.00
2    UNIVERSITA DELLA CALABRIA IT (ARCAVACATA DI RENDE) participant 402˙218.00
3    UNIVERSITAET KLAGENFURT AT (KLAGENFURT) participant 382˙437.00
4    INTEGRIS SOCIETA PER AZIONI IT (ROMA) participant 320˙537.00
5    INSTYTUT CHEMII BIOORGANICZNEJ POLSKIEJ AKADEMII NAUK PL (POZNAN) participant 313˙750.00
6    BULL SAS FR (LES CLAYES SOUS BOIS) participant 297˙875.00
7    INSTITUTUL E-AUSTRIA TIMISOARA RO (TIMISOARA) participant 227˙175.00
8    SERVICIO MADRILENO DE SALUD ES (MADRID) participant 181˙500.00

Map

 Project objective

Extreme Data is an incarnation of Big Data concept distinguished by the massive amounts of data that must be queried, communicated and analyzed in (near) real-time by using a very large number of memory/storage elements and Exascale computing systems. Immediate examples are the scientific data produced at a rate of hundreds of gigabits-per-second that must be stored, filtered and analyzed, the millions of images per day that must be mined (analyzed) in parallel, the one billion of social data posts queried in real-time on an in-memory components database. Traditional disks or commercial storage cannot handle nowadays the extreme scale of such application data.

Following the need of improvement of current concepts and technologies, ASPIDE’s activities focus on data-intensive applications running on systems composed of up to millions of computing elements (Exascale systems). Practical results will include the methodology and software prototypes that will be designed and used to implement Exascale applications.

The ASPIDE project will contribute with the definition of a new programming paradigms, APIs, runtime tools and methodologies for expressing data-intensive tasks on Exascale systems, which can pave the way for the exploitation of massive parallelism over a simplified model of the system architecture, promoting high performance and efficiency, and offering powerful operations and mechanisms for processing extreme data sources at high speed and/or real-time.

 Publications

year authors and title journal last update
List of publications.
2020 Vincenzo De Maio, Dragi Kimovski
Multi-objective scheduling of extreme data scientific workflows in Fog
published pages: 171-184, ISSN: 0167-739X, DOI: 10.1016/j.future.2019.12.054
Future Generation Computer Systems 106 2020-02-18
2020 Roland Matha, Sasko Ristov, Thomas Fahringer, Radu Prodan
Simplified Workflow Simulation on Clouds based on Computation and Communication Noisiness
published pages: 1-1, ISSN: 1045-9219, DOI: 10.1109/tpds.2020.2967662
IEEE Transactions on Parallel and Distributed Systems 2020-02-18
2019 Javier López-Gómez, Javier Fernández Muñoz, David del Rio Astorga, Manuel F. Dolz, J. Daniel Garcia
Exploring stream parallel patterns in distributed MPI environments
published pages: 24-36, ISSN: 0167-8191, DOI: 10.1016/j.parco.2019.03.004
Parallel Computing 84 2020-02-13
2020 Vladislav Kashansky, Dragi Kimovski, Radu Prodan, Prateek Agrawal, Fabrizio Marozzo, Iuhasz Gabriel, Marek Justyna and Javier Garcia-Blas
M3AT: Monitoring Agents Assignment Model for Data-Intensive Applications has been accepted to the Euromicro PDP’2020
published pages: , ISSN: , DOI:
Euromicro PDP’2020 1 2020-02-13
2019 Ioan Drăgan, Gabriel Iuhasz, Dana Petcu
A Scalable Platform for Monitoring Data Intensive Applications
published pages: 503-528, ISSN: 1570-7873, DOI: 10.1007/s10723-019-09483-1
Journal of Grid Computing 17/3 2020-02-13
2019 Domenico Talia
A view of programming scalable data analysis: from clouds to exascale
published pages: , ISSN: 2192-113X, DOI: 10.1186/s13677-019-0127-x
Journal of Cloud Computing 8/1 2020-02-13
2019 Alberto Cascajo, David E. Singh, Jesus Carretero
Performance-Aware Scheduling of Parallel Applications on Non-Dedicated Clusters
published pages: 982, ISSN: 2079-9292, DOI: 10.3390/electronics8090982
Electronics 8/9 2020-02-13
2019 Domenico Talia, Paolo Trunfio, Fabrizio Marozzo, Loris Belcastro, Javier Garcia-Blas, David del Rio, Philippe Couvee, Gael Goret, Lionel Vincent, Alberto Fernandez-Pena, Daniel Martin de Blas, Mirko Nardi, Teresa Pizzuti, Adrian Spataru and Marek Justyna
A novel Data-Centric Programming Model for Large-Scale Parallel Systems
published pages: , ISSN: , DOI:
EuroPAR 2019 1 2020-02-13
2019 Loris Belcastro, Fabrizio Marozzo, Domenico Talia, Paolo Trunfio
ParSoDA: high-level parallel programming for social data mining
published pages: , ISSN: 1869-5450, DOI: 10.1007/s13278-018-0547-5
Social Network Analysis and Mining 9/1 2020-02-13
2019 Javier López-Gómez, David del Rio Astorga, Manuel F. Dolz, Javier Fernández, J. Daniel García
Detecting semantic violations of lock-free data structures through C++ contracts
published pages: , ISSN: 0920-8542, DOI: 10.1007/s11227-019-02827-4
The Journal of Supercomputing 2020-02-13

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