ASPIDE: exAScale ProgramIng models for extreme Data procEssing

Risultati finali

Extreme scale monitoring architecture - Initial

Initial description of the Exascale monitoring architecture.

Extreme scale monitoring architecture - Final

Final description of the Exascale monitoring architecture

Strategies and methodology for data movement in Exascale

Report that presents an the load balance and optimization techniques designed for data movement in Exascale sytems

Dissemination and training report - Second year

Report that describes the workpackages activities results in the second year

Data collection and mining for Exascale systems

Description of the explored data collection and mining methodologies.

Final Report

Report that presents an overview of the activities that were fulfilled during the project duration

Requirements

This deliverable will consists of two-parts; the first in the shape of requirements analysis strategy and the second stage in the shape of the requirement repository.

Paradigms and models at run-time - final report

Report that presents the evaluation of the advance of the stateoftheart by using the WP2 concepts and tools for Exascale systems

Applications design

This report comprises detailed descriptions of the applications, the list of used WP2-WP4 concepts, their modes of integration with the applications, recommendations and APIs that will be taken into account in the redesign process, as well as the key performance indicators for the future evaluation of the improvements in performance through the re-design and re-implementation.

Validation of project outcomes through applications

Final validation and evaluation of the newly developed tools by WP2WP4 using the proposed use cases

Programming paradigms supporting extreme data

Include the concepts and proposals from T2.1 and T2.2.

Auto-tune of Exascale applications

Final report providing detailed description of the developed tools related to the Exascale applications auto tuning

Strategies and methodology for Exascale data management

Description of the predictive and adaptive data layout strategies designed for Exascale.

Dissemination and training report - First year

Report that describes the work packages activities results in the first half of the project.

Techniques for in-memory real-time data analytics support at Exascale level

Report that presents an the load balance and optimization techniques designed for data movement in Exascale sytems

Adaptive analysis of application behaviour

Report that presents an overview of the novel methodologies and tools for adaptive analysis of applica tions behavior

Communication, collaboration, exploitation and standardization report - Third year

Report that describes the results of the activities in the project related to exploitation and standardization

Monitoring data collection and mining for Exascale systems

Description of the explored data collection and mining methodologies.

Web site

Public Web site.

Early tool prototypes for data management

Description of the explored data collection and mining methodologies.

Libraries and tools to support extreme data processing

Collection of software prototypes with internal user manuals as results of T2.1-T2.4.

Applications - alpha version

The first versions of the applications using the software prototypes strategies methodologies and concepts from WP2 WP4 and the feedback for their improvement internal user manuals

Run-time support for programming extreme data application

Runtime system prototype with detailed user manual

Framework for programming extreme data application

Collection of software prototypes with detailed user manuals as results of T21T23

Low-overhead software instrumentation and monitoring

Description of the explored instrumentation and monitoring methodologies and tools.

AIDE framework software

Report that presents the evaluation of the advance of the stateoftheart by using the WP2 concepts and tools for Exascale systems

Pubblicazioni

Exploiting Machine Learning for Improving In-Memory Execution of Data-Intensive Workflows on Parallel Machines

Autori: Riccardo Cantini, Fabrizio Marozzo, Alessio Orsino, Domenico Talia, Paolo Trunfio
Pubblicato in: Future Internet, Numero 13/5, 2021, Pagina/e 121, ISSN 1999-5903
Editore: MDPI
DOI: 10.3390/fi13050121

Do Pregnancy-Induced Brain Changes Reverse? The Brain of a Mother Six Years after Parturition

Autori: Magdalena Martínez-García, María Paternina-Die, Erika Barba-Müller, Daniel Martín de Blas, Laura Beumala, Romina Cortizo, Cristina Pozzobon, Luis Marcos-Vidal, Alberto Fernández-Pena, Marisol Picado, Elena Belmonte-Padilla, Anna Massó-Rodriguez, Agustin Ballesteros, Manuel Desco, Óscar Vilarroya, Elseline Hoekzema, Susanna Carmona
Pubblicato in: Brain Sciences, Numero 11/2, 2021, Pagina/e 168, ISSN 2076-3425
Editore: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/brainsci11020168

The Paternal Transition Entails Neuroanatomic Adaptations that are Associated with the Father’s Brain Response to his Infant Cues

Autori: María Paternina-Die, Magdalena Martínez-García, Clara Pretus, Elseline Hoekzema, Erika Barba-Müller, Daniel Martín de Blas, Cristina Pozzobon, Agustín Ballesteros, Óscar Vilarroya, Manuel Desco, Susanna Carmona
Pubblicato in: Cerebral Cortex Communications, Numero 1/1, 2020, ISSN 2632-7376
Editore: Cerebral Cortex Communications
DOI: 10.1093/texcom/tgaa082

Thalamic atrophy in patients with pure hereditary spastic paraplegia type 4

Autori: Francisco J. Navas-Sánchez, Alberto Fernández-Pena, Daniel Martín de Blas, Yasser Alemán-Gómez, Luís Marcos-Vidal, Juan A. Guzmán-de-Villoria, Pilar Fernández-García, Julia Romero, Irene Catalina, Laura Lillo, José L. Muñoz-Blanco, Andrés Ordoñez-Ugalde, Beatriz Quintáns, Julio Pardo, María-Jesús Sobrido, Susanna Carmona, Francisco Grandas, Manuel Desco
Pubblicato in: Journal of Neurology, Numero 268/7, 2021, Pagina/e 2429-2440, ISSN 0340-5354
Editore: Dr. Dietrich Steinkopff Verlag
DOI: 10.1007/s00415-020-10387-4

Accelerated iterative image reconstruction for cone-beam computed tomography through Big Data frameworks

Autori: Estefania Serrano, Javier Garcia-Blas, Jesus Carretero, Manuel Desco, Monica Abella
Pubblicato in: Future Generation Computer Systems, Numero 106, 2020, Pagina/e 534-544, ISSN 0167-739X
Editore: Elsevier BV
DOI: 10.1016/j.future.2019.12.042

Using social media for sub-event detection during disasters

Autori: Loris Belcastro, Fabrizio Marozzo, Domenico Talia, Paolo Trunfio, Francesco Branda, Themis Palpanas, Muhammad Imran
Pubblicato in: Journal of Big Data, Numero 8/1, 2021, ISSN 2196-1115
Editore: Springer
DOI: 10.1186/s40537-021-00467-1

A Data-Aware Scheduling Strategy for Executing Large-Scale Distributed Workflows

Autori: Salvatore Giampa, Loris Belcastro, Fabrizio Marozzo, Domenico Talia, Paolo Trunfio
Pubblicato in: IEEE Access, Numero 9, 2021, Pagina/e 47354-47364, ISSN 2169-3536
Editore: Institute of Electrical and Electronics Engineers Inc.
DOI: 10.1109/access.2021.3067815

Detecting semantic violations of lock-free data structures through C++ contracts

Autori: Javier López-Gómez, David del Rio Astorga, Manuel F. Dolz, Javier Fernández, J. Daniel García
Pubblicato in: The Journal of Supercomputing, 2019, ISSN 0920-8542
Editore: Kluwer Academic Publishers
DOI: 10.1007/s11227-019-02827-4

A Scalable Platform for Monitoring Data Intensive Applications

Autori: Ioan Drăgan, Gabriel Iuhasz, Dana Petcu
Pubblicato in: Journal of Grid Computing, Numero 17/3, 2019, Pagina/e 503-528, ISSN 1570-7873
Editore: Kluwer Academic Publishers
DOI: 10.1007/s10723-019-09483-1

Exploring stream parallel patterns in distributed MPI environments

Autori: Javier López-Gómez, Javier Fernández Muñoz, David del Rio Astorga, Manuel F. Dolz, J. Daniel Garcia
Pubblicato in: Parallel Computing, Numero 84, 2019, Pagina/e 24-36, ISSN 0167-8191
Editore: Elsevier BV
DOI: 10.1016/j.parco.2019.03.004

ParSoDA: high-level parallel programming for social data mining

Autori: Loris Belcastro, Fabrizio Marozzo, Domenico Talia, Paolo Trunfio
Pubblicato in: Social Network Analysis and Mining, Numero 9/1, 2019, ISSN 1869-5450
Editore: Springer
DOI: 10.1007/s13278-018-0547-5

Performance-Aware Scheduling of Parallel Applications on Non-Dedicated Clusters

Autori: Alberto Cascajo, David E. Singh, Jesus Carretero
Pubblicato in: Electronics, Numero 8/9, 2019, Pagina/e 982, ISSN 2079-9292
Editore: MDPI
DOI: 10.3390/electronics8090982

A view of programming scalable data analysis: from clouds to exascale

Autori: Domenico Talia
Pubblicato in: Journal of Cloud Computing, Numero 8/1, 2019, ISSN 2192-113X
Editore: Springer Science + Business Media
DOI: 10.1186/s13677-019-0127-x

Multi-objective scheduling of extreme data scientific workflows in Fog

Autori: Vincenzo De Maio, Dragi Kimovski
Pubblicato in: Future Generation Computer Systems, Numero 106, 2020, Pagina/e 171-184, ISSN 0167-739X
Editore: Elsevier BV
DOI: 10.1016/j.future.2019.12.054

Simplified Workflow Simulation on Clouds based on Computation and Communication Noisiness

Autori: Roland Matha, Sasko Ristov, Thomas Fahringer, Radu Prodan
Pubblicato in: IEEE Transactions on Parallel and Distributed Systems, 2020, Pagina/e 1-1, ISSN 1045-9219
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tpds.2020.2967662

Kulla, a container-centric construction model for building infrastructure-agnostic distributed and parallel applications

Autori: Hugo G. Reyes-Anastacio, J.L Gonzalez-Compean, Victor J. Sosa-Sosa, Jesus Carretero, Javier Garcia-Blas
Pubblicato in: Journal of Systems and Software, Numero 168, 2020, Pagina/e 110665, ISSN 0164-1212
Editore: Elsevier BV
DOI: 10.1016/j.jss.2020.110665

Towards enhanced MRI by using a multiple back end programming framework

Autori: Javier Garcia-Blas, David del Rio Astorga, Jesus Carretero, J. Daniel Garcia
Pubblicato in: Future Generation Computer Systems, Numero 112, 2020, Pagina/e 467-477, ISSN 0167-739X
Editore: Elsevier BV
DOI: 10.1016/j.future.2020.05.039

Exposing data locality in HPC-based systems by using the HDFS backend

Autori: Jose Rivadeneira, Felix Garcia-Carballeira, Jesus Carretero, Javier Garcia-Blas
Pubblicato in: 2020 IEEE 27th International Conference on High Performance Computing, Data, and Analytics (HiPC), 2020, Pagina/e 243-250, ISBN 978-1-6654-2292-5
Editore: IEEE
DOI: 10.1109/hipc50609.2020.00038

Perspectives on Anomaly and Event Detection in Exascale Systems

Autori: Gabriel Iuhasz, Dana Petcu
Pubblicato in: 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), 2019, Pagina/e 225-229, ISBN 978-1-7281-0006-7
Editore: IEEE
DOI: 10.1109/bigdatasecurity-hpsc-ids.2019.00051

M3AT: Monitoring Agents Assignment Model for Data-Intensive Applications

Autori: Vladislav Kashansky, Dragi Kimovski, Radu Prodan, Prateek Agrawal, Fabrizio Marozzo, Gabriel Iuhasz, Marek Marozzo, Javier Garcia-Blas
Pubblicato in: 2020 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), 2020, Pagina/e 72-79, ISBN 978-1-7281-6582-0
Editore: IEEE
DOI: 10.1109/pdp50117.2020.00018

A novel Data-Centric Programming Model for Large-Scale Parallel Systems

Autori: 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
Pubblicato in: EuroPAR 2019, Numero 1, 2019
Editore: Springer

Exploiting Stream Parallelism of MRI Reconstruction Using GrPPI over Multiple Back-Ends

Autori: Javier Garcia-Blas, David del Rio Astorga, J. Daniel Garcia, Jesus Carretero
Pubblicato in: 2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), 2019, Pagina/e 631-637, ISBN 978-1-7281-0912-1
Editore: IEEE
DOI: 10.1109/ccgrid.2019.00081

Monitoring of Exascale data processing

Autori: Gabriel Iuhasz, Dana Petcu
Pubblicato in: 2019 IEEE International Conference on Advanced Scientific Computing (ICASC), 2019, Pagina/e 1-5, ISBN 978-1-7281-4454-2
Editore: IEEE
DOI: 10.1109/icasc48083.2019.8946279

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