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Enabling dynamic and Intelligent workflows in the future EuroHPCecosystem

CORDIS bietet Links zu öffentlichen Ergebnissen und Veröffentlichungen von HORIZONT-Projekten.

Links zu Ergebnissen und Veröffentlichungen von RP7-Projekten sowie Links zu einigen Typen spezifischer Ergebnisse wie Datensätzen und Software werden dynamisch von OpenAIRE abgerufen.

Leistungen

Application bottlenecks and optimization opportunities on heterogeneous components (öffnet in neuem Fenster)

This report will include the identified bottlenecks as well as a preliminary analysis of such issues when considering the communication architecture, the storage architecture, and the use of heterogeneous components. This report will be the baseline for the work of the remaining tasks in this WP. This deliverable will collect theoutcome from task T3.1.

Design of the Pillar I use cases (öffnet in neuem Fenster)

Report on the choice of industrially relevant problems to be addressed in the WP. Report will identify success criteria as well as potential breakthroughs

Revision of Requirements and architecture design (öffnet in neuem Fenster)

Second version of the Pillars’ requirements and architecture design (output from tasks 1.1, 1.2, 4.1, 5.1, 6.1)

Requirements on the eFlows4HPC software stack from Pillar II and evaluation metrics. (öffnet in neuem Fenster)

This report will include a prioritized list of requirements that will guide the design and implementation of the different Pillar II use cases as well as of the core eFlows4HPC components. The deliverable will also include a set of metrics to be used in the evaluation of the developed workflows

Report on Implementing Containerization and Optimization Strategy (öffnet in neuem Fenster)

This deliverable will describe the implementation of the Optimization Strategy (D2.2) and possibly reevaluate the approach taken. Tasks 2.1, 2.2, 2.3 contribute to this deliverable.

Optimized kernels for heterogeneous components (öffnet in neuem Fenster)

This document will reflect the final version of the optimized kernels developed in the project for the use of heterogeneous components. It will compound application kernels and neural network kernels. This deliverable will collect the work from tasks T3.2and T3.4, including associated evaluation work from T3.6.

Second Dissemination and Communication Report (öffnet in neuem Fenster)

This deliverable will report on the dissemination and communication activities of the project done during the second year.

Validation of the Pillar I use cases (öffnet in neuem Fenster)

Report on the validation of the ROM models. Evaluation of success criteria.

Requirements on the eFlows4HPC software stack from Pillar I and evaluation metrics (öffnet in neuem Fenster)

Summary of conclusions on the pillar I requirements.

Optimized data management with new storage technologies (öffnet in neuem Fenster)

This document will reflect the optimizations performed with new storage technologies. This deliverable will collect the work from task T3.5 and associated evaluation work from T3.6.

Optimized kernels for EPI (öffnet in neuem Fenster)

In this report the optimized kernels for the EPI will be described. Their implementation on the emulated hardware platform will be provided and its projected performance and energy saving. This deliverable will collect the work from task T3.3, including associatedevaluation work from T3.6.

Initial draft of optimized kernels for EPI (öffnet in neuem Fenster)

In this report the draft version of the optimized kernels for the EPI will be described. Their implementation on the emulated hardware platform will be provided and its projected performance and energy saving. This deliverable will collect the work from task T3.3, including associated evaluation work from T3.6.

Final Report on Data Logistics Implementation (öffnet in neuem Fenster)

This report will comprise the description of the final version of Data Logistics Service, implementation of data pipelines motivated by Pillars. Task 2.3 and 2.4 contribute to this deliverable.

Training Plan (öffnet in neuem Fenster)

This deliverable will define the objectives of the project's training activities, the initial plans for organization of the training activities, as well as the materials that will be provided.

Technology Evaluation, Containerization and Optimization Strategy (öffnet in neuem Fenster)

Based on the available requirements, this deliverable will derive a strategy for optimizing deployment in all envisioned dimensions: by optimizing libraries and runtimes, using containers, and application of emerging storage solutions. Tasks 2.1, 2.2, 2.3 contribute to this deliverable.

Validation of requirements (öffnet in neuem Fenster)

This report will include the process and outcome of the validation of the requirements (internal and external evaluations).

Initial draft of optimized kernels for heterogeneous components (öffnet in neuem Fenster)

This document will reflect a first draft version of the optimized kernels developed in the project for the use of heterogeneous components. It will compound application kernels and neural network kernels. This deliverable will collect the work from tasks T3.2 and T3.4, including associated evaluation work from T3.6.

Requirements on the eFlows4HPC software stack from Pillar III and evaluation metrics (öffnet in neuem Fenster)

Summary of conclusions on the pillar III requirements.

Report of the organization of community workshops (öffnet in neuem Fenster)

This deliverable will report on the community workshops organized during the lifetime of the project.

Protocol for urgent HPC (öffnet in neuem Fenster)

Protocol definition and governance recommendations regarding urgent HPC.

Dissemination and Communication Plan (öffnet in neuem Fenster)

This deliverable will set out the dissemination and communication strategy and the activities to be undertaken to achieve this. Results of the dissemination work will be reported in the periodic and final reports.

Description of the use cases for Pillar III (öffnet in neuem Fenster)

Compendium of datasets and models employed for development and validation of Pillar III workflows

First Dissemination and Communication Report (öffnet in neuem Fenster)

This deliverable will report on the dissemination and communication activities of the project done during the first year.

Design of the Pillar II use cases (öffnet in neuem Fenster)

This report provides a complete design and comprehensive documentation of the software architecture of the Pillar II use cases

Final Dissemination and Communication Report (öffnet in neuem Fenster)

This deliverable will report on the final dissemination and communication activities of the complete project.

Requirements, metrics and architecture design (öffnet in neuem Fenster)

First version of the pillar’srequirements, evaluation metrics and architecture design (output from tasks 1.1, 1.2, 4.1, 5.1, 6.1)

eFlows4HPC interfaces and Iteration 1 software stack release (öffnet in neuem Fenster)

First version of the design and implementation of the eFlows4HPC software stack interfaces. Tasks 1.3, 1.4, 1.5 contribute to this deliverable.

Database of Earth models (öffnet in neuem Fenster)

Release of Earth models obtained for the usecase regions, together with associated metadata.

Pillar II - Iteration 2 Software Release (öffnet in neuem Fenster)

This deliverable relates to the software and documentation released at the end of Iteration 2 for the implementation of the Pillar II use cases.

First version of Data Logistics (öffnet in neuem Fenster)

This deliverable will be the first production version of Data Logistics service integration with selected storage technology and demonstration of a data pipeline motivated by the Pillars’ use cases. Task 2.3 and 2.4 contribute to this deliverable.

ROM Tools Release (öffnet in neuem Fenster)

Code Release of essential tools for ROM preparation

Data Catalogue (öffnet in neuem Fenster)

Based on the requirements report D1.1 this deliverable will analyse and describe the data sources used by the Pillars. This information will be made available in the form of an electronic document or service.

eFlows4HPC interfaces and final software stack release (öffnet in neuem Fenster)

Second version of the design and implementation of the eFlows4HPC software stack interfaces. Tasks 1.3, 1.4, 1.5, 1.6 and 1.7 contribute to this deliverable.

Iteration 1 workflows for urgent computing of natural hazards (öffnet in neuem Fenster)

Taskbased version of UCIS4EQ and PTF workflows.

Demo ROM (öffnet in neuem Fenster)

Release of demonstrator ROM model.

eFlows4HPC interfaces and Iteration 2 software stack release (öffnet in neuem Fenster)

Second version of the design and implementation of the eFlows4HPC software stack interfaces. Tasks 1.3, 1.4, 1.5, and 1.6 contribute to this deliverable.

Pillar II - Iteration 1 Software Release (öffnet in neuem Fenster)

This deliverable relates to the software and documentation released at the end of Phase 1 for the implementation of the Pillar II use cases

Iteration 2 workflows for urgent computing of natural hazards (öffnet in neuem Fenster)

Final releases of the UCIS4EQ and PTF workflows.

Release of HPCWaaS integrated solver stack (öffnet in neuem Fenster)

Release of software stack integrated in the HPCWaaS interface

Data Management Plan (öffnet in neuem Fenster)

Document outlining how data will be managed during the project from internal and external point of view. The DMP will include a table specifying how the datawill be exploited, shared for verification and reuse. Updates to this report will be provided in M12, M24 and M36.

Veröffentlichungen

Use of Neural Networks for Tsunami Maximum Height and Arrival Time Predictions (öffnet in neuem Fenster)

Autoren: Rodríguez, J.F.; Macías, J.; Castro, M.J.; de la Asunción, M.; Sánchez-Linares, C. Use of Neural Networks for Tsunami Maximum Height and Arrival Time Predictions. GeoHazards 2022, 3, 323-344. 
Veröffentlicht in: GeoHazards, Ausgabe 3(2), 2022, ISSN 2624-795X
Herausgeber: MDPI
DOI: 10.3390/geohazards3020017

A Shape Optimization Pipeline for Marine Propellers by means of Reduced Order Modeling Techniques (öffnet in neuem Fenster)

Autoren: Ivagnes, Anna; Demo, Nicola; Rozza, Gianluigi
Veröffentlicht in: The International Journal for Numerical Methods in Engineering, 2024, ISSN 0029-5981
Herausgeber: John Wiley & Sons Inc.
DOI: 10.48550/arxiv.2305.07515

Fast truncated SVD of sparse and dense matrices on graphics processors (öffnet in neuem Fenster)

Autoren: Andrés E. Tomás; Enrique S. Quintana-Orti; Hartwig Anzt
Veröffentlicht in: The International Journal of High Performance Computing Applications, 2023, ISSN 1094-3420
Herausgeber: SAGE Publications
DOI: 10.1177/10943420231179699

Reformulating the direct convolution for high-performance deep learning inference on ARM processors (öffnet in neuem Fenster)

Autoren: Sergio Barrachina, Adrián Castelló, Manuel F. Dolz, Tze Meng Low, Héctor Martínez, Enrique S. Quintana-Ortí, Upasana Sridhar, Andrés E. Tomás,
Veröffentlicht in: Journal of Systems Architecture, 2023, ISSN 1383-7621
Herausgeber: Elsevier BV
DOI: 10.1016/j.sysarc.2022.102806

Geometrically Parametrised Reduced Order Models for Studying the Hysteresis of the Coanda Effect in Finite-elements-based Incompressible Fluid Dynamics (öffnet in neuem Fenster)

Autoren: Bravo, J. & Stabile, Giovanni & Hess, M. & Hernández, Joaquin & Rossi, R. & Rozza, Gianluigi.
Veröffentlicht in: Journal of Computational Physics, 2023, ISSN 0021-9991
Herausgeber: Academic Press
DOI: 10.48550/arxiv.2307.05227

Enhancing iteration performance on distributed task-based workflows (öffnet in neuem Fenster)

Autoren: Alex Barcelo; Anna Queralt; Toni Cortes
Veröffentlicht in: Distributed, Parallel, and Cluster Computing, Ausgabe Volume 149, 2023, Seite(n) 359-375, ISSN 0167-739X
Herausgeber: Elsevier BV
DOI: 10.1016/j.future.2023.07.032

Empirical Interscale Finite Element Method (EIFEM) for modeling heterogeneous structures via localized hyperreduction (öffnet in neuem Fenster)

Autoren: J.A. Hernández, A. Giuliodori, E. Soudah,
Veröffentlicht in: Computer Methods in Applied Mechanics and Engineering, Ausgabe Volume 418, Part A,, 2024, ISSN 0045-7825
Herausgeber: Elsevier BV
DOI: 10.1016/j.cma.2023.116492

Block size estimation for data partitioning in HPC applications using machine learning techniques (öffnet in neuem Fenster)

Autoren: Riccardo Cantini, Fabrizio Marozzo, Alessio Orsino, Domenico Talia, Paolo Trunfio, Rosa M. Badia, Jorge Ejarque & Fernando Vázquez-Novoa
Veröffentlicht in: JournalofBigData, 2024, ISSN 2196-1115
Herausgeber: JournalofBigData
DOI: 10.1186/s40537-023-00862-w

Dynamic resource allocation for efficient parallel CFD simulations (öffnet in neuem Fenster)

Autoren: G. Houzeaux; R.M. Badia; R. Borrell; D. Dosimont; J. Ejarque; M. Garcia-Gasulla; V. López
Veröffentlicht in: Distributed, Parallel, and Cluster Computing (cs.DC), 2021, ISSN 0045-7930
Herausgeber: Pergamon Press Ltd.
DOI: 10.1016/j.compfluid.2022.105577

Urgent Computing for Protecting People From Natural Disasters (öffnet in neuem Fenster)

Autoren: Domenico Talia, Paolo Trunfio
Veröffentlicht in: Computer, Ausgabe Volume: 56, Ausgabe: 4,, 2023, ISSN 0018-9162
Herausgeber: Institute of Electrical and Electronics Engineers
DOI: 10.1109/mc.2023.3241733

Pyophidia: a python library for high performance data analytics at scale. (öffnet in neuem Fenster)

Autoren: Donatello Elia, Cosimo Palazzo, Sandro Fiore, Alessandro D’Anca, Andrea Mariello, Giovanni Aloisio
Veröffentlicht in: SoftwareX, Ausgabe Volume 24, 2023, ISSN 2352-7110
Herausgeber: SoftwareX
DOI: 10.1016/j.softx.2023.101538

A comparison of data-driven reduced order models for the simulation of mesoscale atmospheric flow (öffnet in neuem Fenster)

Autoren: Arash Hajisharifi; Michele Girfoglio; Annalisa Quaini; Gianluigi Rozza
Veröffentlicht in: Finite Elements in Analysis and Design, Ausgabe 228, 2024, ISSN 0168-874X
Herausgeber: Elsevier BV
DOI: 10.48550/arxiv.2307.08790

Programming parallel dense matrix factorizations and inversion for new-generation NUMA architectures (öffnet in neuem Fenster)

Autoren: Sandra Catalán, Francisco D. Igual, José R. Herrero, Rafael Rodríguez-Sánchez, Enrique S. Quintana-Ortí,
Veröffentlicht in: Journal of Parallel and Distributed Computing, 2023, Seite(n) 51-65, ISSN 0743-7315
Herausgeber: Academic Press
DOI: 10.1016/j.jpdc.2023.01.004

An Ensemble Machine Learning Approach for Tropical Cyclone Localization and Tracking From ERA5 Reanalysis Data (öffnet in neuem Fenster)

Autoren: Accarino, Gabriele; Donno, Davide; Immorlano, Francesco; Elia, Donatello; Aloisio, Giovanni
Veröffentlicht in: Earth and Space Science, Ausgabe 23, 2023, Seite(n) Volume 10, Ausgabe 11, ISSN 2333-5084
Herausgeber: American Geophysical Union (AGU)
DOI: 10.1029/2023ea003106

Multiscale modeling of prismatic heterogeneous structures based on a localized hyperreduced-order method (öffnet in neuem Fenster)

Autoren: A. Giuliodori, J.A. Hernández, E. Soudah,
Veröffentlicht in: Computer Methods in Applied Mechanics and Engineering, Ausgabe Volume 407, 2023, ISSN 0045-7825
Herausgeber: Elsevier BV
DOI: 10.1016/j.cma.2023.115913

Toward Matrix Multiplication for Deep Learning Inference on the Xilinx Versal (öffnet in neuem Fenster)

Autoren: Lei, Jie; Flich, José; Quintana-Ort, Enrique S.
Veröffentlicht in: Euromicro Conference on Parallel, Distributed and Network-Based Processing 2023, 2023, ISSN 2377-5750
Herausgeber: IEEE
DOI: 10.1109/pdp59025.2023.00043

A continuous convolutional trainable filter for modelling unstructured data (öffnet in neuem Fenster)

Autoren: Coscia, D; Meneghetti, L; Demo, N; Stabile, G; Rozza, G
Veröffentlicht in: Computational Mechanics, Ausgabe 72, 2023, Seite(n) 253–265, ISSN 0178-7675
Herausgeber: Springer Verlag
DOI: 10.1007/s00466-023-02291-1

A BLIS-like matrix multiplication for machine learning in the RISC-V ISA-based GAP8 processor (öffnet in neuem Fenster)

Autoren: C. Ramirez, Adrián Castelló, Enrique S Quintana-Orti
Veröffentlicht in: The Journal of Supercomputing, 2022, ISSN 0920-8542
Herausgeber: Kluwer Academic Publishers
DOI: 10.1007/s11227-022-04581-6

CECM: A continuous empirical cubature method with application to the dimensional hyperreduction of parameterized finite element models (öffnet in neuem Fenster)

Autoren: J.A. Hernández, J.R. Bravo, S. Ares de Parga
Veröffentlicht in: Computer Methods in Applied Mechanics and Engineering, Ausgabe Volume 418, Part B,, 2024, ISSN 0045-7825
Herausgeber: Elsevier BV
DOI: 10.1016/j.cma.2023.116552

An enriched finite element/level-set model for two-phase electrohydrodynamic simulations (öffnet in neuem Fenster)

Autoren: Christian Narváez-Muñoz; Mohammad R. Hashemi; Pavel B. Ryzhakov; Jordi Pons-Prats
Veröffentlicht in: Physics of Fluids, Ausgabe 6, 2023, Seite(n) 35, ISSN 1089-7666
Herausgeber: AIP Publishing
DOI: 10.1063/5.0127274

An Ensemble Machine Learning Approach for Tropical Cyclone Detection Using ERA5 Reanalysis Data (öffnet in neuem Fenster)

Autoren: Accarino, Gabriele; Donno, Davide; Immorlano, Francesco; Elia, Donatello; Aloisio, Giovanni
Veröffentlicht in: Earth and Space Science, Ausgabe 3, 2023, ISSN 2333-5084
Herausgeber: AGU Journals
DOI: 10.48550/arxiv.2306.07291

A kinematically stabilized linear tetrahedral finite element for compressible and nearly incompressible finite elasticity (öffnet in neuem Fenster)

Autoren: Guglielmo Scovazzi; Rubén Zorrilla; Riccardo Rossi
Veröffentlicht in: Computer Methods in Applied Mechanics and Engineering, Ausgabe Volume 412, 2023, ISSN 0045-7825
Herausgeber: Elsevier BV
DOI: 10.1016/j.cma.2023.116076

Enabling dynamic and intelligent workflows for HPC, data analytics, and AI convergence (öffnet in neuem Fenster)

Autoren: Jorge Ejarque; Rosa M. Badia; Loïc Albertin; Giovanni Aloisio; Enrico Baglione; Yolanda Becerra; Stefan Boschert; Julian R. Berlin; Alessandro D’Anca; Donatello Elia; François Exertier; Sandro Fiore; José Flich; Arnau Folch; Steven J. Gibbons; Nikolay Koldunov; Francesc Lordan; Stefano Lorito; Finn Løvholt; Jorge Macías; Fabrizio Marozzo; Alberto Michelini; Marisol Monterrubio-Velasco; Mart
Veröffentlicht in: EPIC3Future Generation Computer Systems, Ausgabe 134, 2022, Seite(n) 414-429, ISSN 0167-739X
Herausgeber: Elsevier BV
DOI: 10.1016/j.future.2022.04.014

Programming Big Data Analysis: Principles and Solutions (öffnet in neuem Fenster)

Autoren: Loris Belcastro, Riccardo Cantini, Fabrizio Marozzo, Alessio Orsino, Domenico Talia & Paolo Trunfio
Veröffentlicht in: Journal of Big Data, Ausgabe 9:4, 2022, ISSN 2196-1115
Herausgeber: SpringerOpen
DOI: 10.1186/s40537-021-00555-2

PyCOMPSs as an instrument for Translational Computer Science (öffnet in neuem Fenster)

Autoren: Rosa M. Badia; Javier Conejero; Jorge Ejarque; Daniele Lezzi; Francesc Lordan
Veröffentlicht in: Computing in Science & Engineering, Ausgabe 24(2), 2022, ISSN 1558-366X
Herausgeber: IEEE
DOI: 10.22541/au.164557536.67201934/v1

Automatizing the creation of specialized high-performance computing containers (öffnet in neuem Fenster)

Autoren: Jorge Ejarque; Rosa M Badia
Veröffentlicht in: The International Journal of High Performance Computing Applications, 2023, ISSN 1094-3420
Herausgeber: SAGE Publications
DOI: 10.1177/10943420231165729

Revisiting active object stores: Bringing data locality to the limit with NVM (öffnet in neuem Fenster)

Autoren: Alex Barceló; Anna Queralt; Anna Queralt; Toni Cortes; Toni Cortes
Veröffentlicht in: Future Generation Computer Systems, Ausgabe Volume 129, 2021, Seite(n) 425-439, ISSN 0167-739X
Herausgeber: Elsevier BV
DOI: 10.1016/j.future.2021.10.025

Sparse matrix‐vector and matrix‐multivector products for the truncated SVD on graphics processors (öffnet in neuem Fenster)

Autoren: José I. Aliaga; Hartwig Anzt; Enrique S. Quintana‐Ortí; Andrés E. Tomás
Veröffentlicht in: Concurrency and Computation: Practice and Experience, 2023, ISSN 1532-0626
Herausgeber: John Wiley & Sons Inc.
DOI: 10.1002/cpe.7871

Boosting HPC data analysis performance with the ParSoDA-Py library (öffnet in neuem Fenster)

Autoren: Belcastro, L., Giampà, S., Marozzo, F,Rosa M. Badia, Jorge Ejarque & Nihad Mammadli,Loris Belcastro, Salvatore Giampà, Fabrizio Marozzo, Domenico Talia & Paolo Trunfio
Veröffentlicht in: The Journal of Supercomputing, 2024, ISSN 0920-8542
Herausgeber: Kluwer Academic Publishers
DOI: 10.1007/s11227-023-05883-z

Generative Adversarial Reduced Order Modelling (öffnet in neuem Fenster)

Autoren: Coscia, Dario; Demo, Nicola; Rozza, Gianluigi
Veröffentlicht in: Sci Rep, Ausgabe 14, 2024, ISSN 2045-2322
Herausgeber: Nature Publishing Group
DOI: 10.48550/arxiv.2305.15881

A memory-efficient MultiVector Quasi-Newton method for black-box Fluid-Structure Interaction coupling (öffnet in neuem Fenster)

Autoren: Zorrilla Martínez, Rubén; Rossi, Riccardo
Veröffentlicht in: Computers & Structures, Ausgabe 275, 2022, ISSN 0045-7949
Herausgeber: Pergamon Press Ltd.
DOI: 10.1016/j.compstruc.2022.106934

A Community Roadmap for Scientific Workflows Research and Development (öffnet in neuem Fenster)

Autoren: Rafael Ferreira da Silva, Henri Casanova, Kyle Chard, Ilkay Altintas, Rosa M Badia, Bartosz Balis, Tainã Coleman, Frederik Coppens, Frank Di Natale, Bjoern Enders, Thomas Fahringer, Rosa Filgueira, Grigori Fursin, Daniel Garijo, Carole Goble, Dorran Howell, Shantenu Jha, Daniel S. Katz, Daniel Laney, Ulf Leser, Maciej Malawski, Kshitij Mehta, Loïc Pottier, Jonathan Ozik, J. Luc Peterson, Lavanya
Veröffentlicht in: Rafael Ferreira da Silva, Henri Casanova, Kyle Chard, Ilkay Altintas, Rosa M Badia, Bartosz Balis, Tainã Coleman, Frederik Coppens, Frank Di Natale, Bjoern Enders, Thomas Fahringer, Rosa Filgueira, Grigori Fursin, Daniel Garijo, Carole Goble, Dorran Howell, Shantenu Jha, Daniel S. Katz, Daniel Laney, Ulf Leser, Maciej Malawski, Kshitij Mehta, Loïc Pottier, Jonathan Ozik, J. Luc Peterson, Lavanya, 2021, ISSN 978-1-6654-1136
Herausgeber: IEEE
DOI: 10.1109/works54523.2021.00016

Towards Efficient Neural Network Model Parallelism on Multi-FPGA Platforms (öffnet in neuem Fenster)

Autoren: Rodríguez-Agut, David; Tornero-Gavilá, Rafael; Flich Cardo, José
Veröffentlicht in: 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE), Ausgabe 1, 2023
Herausgeber: IEEE
DOI: 10.23919/date56975.2023.10137117

End-to-End Workflows for Climate Science: Integrating HPC Simulations, Big Data Processing, and Machine Learning (öffnet in neuem Fenster)

Autoren: Donatello Elia; Sonia Scardigno; Jorge Ejarque; Alessandro D’Anca; Gabriele Accarino; Enrico Scoccimarro; Davide Donno; Daniele Peano; Francesco Immorlano; Giovanni Aloisio
Veröffentlicht in: SC-W '23: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, Ausgabe 1, 2023
Herausgeber: Association for Computing Machinery (ACM)
DOI: 10.1145/3624062.3624283

Convolution Operators for Deep Learning Inference on the Fujitsu A64FX Processor (öffnet in neuem Fenster)

Autoren: M. F. Dolz H. Martínez P. Alonso E. S. Quintana-Ortí
Veröffentlicht in: 2022, ISBN 978-1-6654-5155-0
Herausgeber: IEEE
DOI: 10.1109/sbac-pad55451.2022.00027

A Community Roadmap for Scientific Workflows Research and Development (öffnet in neuem Fenster)

Autoren: Rafael Ferreira da Silva, Henri Casanova, Kyle Chard, Ilkay Altintas, Rosa M Badia, Bartosz Balis, Tainã Coleman, Frederik Coppens, Frank Di Natale, Bjoern Enders, Thomas Fahringer, Rosa Filgueira, Grigori Fursin, Daniel Garijo, Carole Goble, Dorran Howell, Shantenu Jha, Daniel S. Katz, Daniel Laney, Ulf Leser, Maciej Malawski, Kshitij Mehta, Loïc Pottier, Jonathan Ozik, J. Luc Peterson, Lavanya
Veröffentlicht in: 2021 IEEE Workshop on Workflows in Support of Large-Scale Science, 2021, ISBN 978-1-6654-1137-0
Herausgeber: IEEE
DOI: 10.48550/arxiv.2110.02168

11th EGU Galileo Conference: Solid Earth and Geohazards in the Exascale Era Consensual Document (öffnet in neuem Fenster)

Autoren: Folch, Arnau; Bhihe, Cedric; Caviedes-Vouillième, Daniel; de la Puente, Josep; Esposti Ongaro, Tomaso; Garg, Deepak; Gibbons, Steven J.; Kaus, Boris; Monterrubio, Marisol; Räss, Ludovic; Reis, Claudia; Scaini, Chiara; Srivastava, Nishtha; Vilarrasa, Víctor; Zwinger, Thomas
Veröffentlicht in: 11th EGU Galileo Conference: Solid Earth and Geohazards in the Exascale Era Consensual document, 2023
Herausgeber: CSIC
DOI: 10.20350/digitalcsic/15439

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