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2. Enabling Dynamic and Intelligent Workflows for HPC, Data Analytics, and AI Convergence

Author

Ejarque, Jorge, Badia, Rosa M., Albertin, Loïc, Aloisio, Giovanni, Baglione, Enrico, Becerra, Yolanda, Boschert, Stefan, Berlin, Julian R., D'Anca, Alessandro, Elia, Donatello, Exertier, François, Fiore, Sandro, Flich, José, Folch, Arnau, Gibbons, Steven J, Koldunov, Nikolay, Lordan, Francesc, Lorito, Stefano, Løvholt, Finn, Macías, Jorge, Marozzo, Fabrizio, Michelini, Alberto, Monterrubio-Velasco, Marisol, Pienkowska, Marta, de la Puente, Josep, Queralt, Anna, Quintana-Ortí, Enrique S., Rodríguez, Juan E., Romano, Fabrizio, Rossi, Riccardo, Rybicki, Jedrzej, Kupczyk, Miroslaw, Selva, Jacopo, Talia, Domenico, Tonini, Roberto, Trunfio, Paolo, and Volp, Manuela

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

The evolution of High-Performance Computing (HPC) platforms enables the design and execution of progressively larger and more complex workflow applications in these systems. The complexity comes not only from the number of elements that compose the workflows but also from the type of computations they perform. While traditional HPC workflows target simulations and modelling of physical phenomena, current needs require in addition data analytics (DA) and artificial intelligence (AI) tasks. However, the development of these workflows is hampered by the lack of proper programming models and environments that support the integration of HPC, DA, and AI, as well as the lack of tools to easily deploy and execute the workflows in HPC systems. To progress in this direction, this paper presents use cases where complex workflows are required and investigates the main issues to be addressed for the HPC/DA/AI convergence. Based on this study, the paper identifies the challenges of a new workflow platform to manage complex workflows. Finally, it proposes a development approach for such a workflow platform addressing these challenges in two directions: first, by defining a software stack that provides the functionalities to manage these complex workflows; and second, by proposing the HPC Workflow as a Service (HPCWaaS) paradigm, which leverages the software stack to facilitate the reusability of complex workflows in federated HPC infrastructures. Proposals presented in this work are subject to study and development as part of the EuroHPC eFlows4HPC project.

Published
2022
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3. The EU Center of Excellence for Exascale in Solid Earth (ChEESE): Implementation, results, and roadmap for the second phase

Author

Folch, Arnau, Abril, Claudia, Afanasiev, Michael, Amati, Giorgio, Bader, Michael, Badia, Rosa M., Bayraktar, Hafize B., Barsotti, Sara, Basili, Roberto, Bernardi, Fabrizio, Boehm, Christian, Brizuela, Beatriz, Brogi, Federico, Cabrera, Eduardo, Casarotti, Emanuele, Castro, Manuel J., Cerminara, Matteo, Cirella, Antonella, Cheptsov, Alexey, Conejero, Javier, Costa, Antonio, de la Asunción, Marc, de la Puente, Josep, Djuric, Marco, Dorozhinskii, Ravil, Espinosa, Gabriela, Esposti-Ongaro, Tomaso, Farnós, Joan, Favretto-Cristini, Nathalie, Fichtner, Andreas, Fournier, Alexandre, Gabriel, Alice-Agnes, Gallard, Jean-Matthieu, Gibbons, Steven J., Glimsdal, Sylfest, González-Vida, José Manuel, Gracia, Jose, Gregorio, Rose, Gutierrez, Natalia, Halldorsson, Benedikt, Hamitou, Okba, Houzeaux, Guillaume, Jaure, Stephan, Kessar, Mouloud, Krenz, Lukas, Krischer, Lion, Laforet, Soline, Lanucara, Piero, Li, Bo, Lorenzino, Maria Concetta, Lorito, Stefano, Løvholt, Finn, Macedonio, Giovanni, Macías, Jorge, Marín, Guillermo, Martínez Montesinos, Beatriz, Mingari, Leonardo, Moguilny, Geneviève, Montellier, Vadim, Monterrubio-Velasco, Marisol, Moulard, Georges Emmanuel, Nagaso, Masaru, Nazaria, Massimo, Niethammer, Christoph, Pardini, Federica, Pienkowska, Marta, Pizzimenti, Luca, Poiata, Natalia, Rannabauer, Leonhard, Rojas, Otilio, Rodriguez, Juan Esteban, Romano, Fabrizio, Rudyy, Oleksandr, Ruggiero, Vittorio, Samfass, Philipp, Sánchez-Linares, Carlos, Sanchez, Sabrina, Sandri, Laura, Scala, Antonio, Schaeffer, Nathanael, Schuchart, Joseph, Selva, Jacopo, Sergeant, Amadine, Stallone, Angela, Taroni, Matteo, Thrastarson, Solvi, Titos, Manuel, Tonelllo, Nadia, Tonini, Roberto, Ulrich, Thomas, Vilotte, Jean-Pierre, Vöge, Malte, Volpe, Manuela, Aniko Wirp, Sara, and Wössner, Uwe

Published
2023
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5. DT-GEO Deliverable: D7.1 Early definition of requirements and specifications DTC-E

Author

European Commission, Kemper, Johannes [0000-0003-1841-9590], Kemper, Johannes, Pienkowska, Marta, Cua, Georgia, Boehm, Christian, Behrens, Jörn, DT-GEO, European Commission, Kemper, Johannes [0000-0003-1841-9590], Kemper, Johannes, Pienkowska, Marta, Cua, Georgia, Boehm, Christian, Behrens, Jörn, and DT-GEO

Abstract

Early definition of requirements and specifications for work package 7 DTC-Es: requirements, specifications and interoperability of earthquake components. Collects and harmonizes the requirements and specifications for DTC-E and its individual modules developed in tasks 7.2 to 7.7.

Published
2024

6. A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern California

Author

Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Barcelona Supercomputing Center, Monterrubio Velasco, Marisol, Callaghan, Scott, Modesto Galende, David, Carrasco Jiménez, José Carlos, Badia Sala, Rosa Maria, Pallarés Font de Mora, Pablo, Vázquez Novoa, Fernando, Quintana Ortí, Enrique Salvador, Pienkowska, Marta, de la Puente, Josep, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Barcelona Supercomputing Center, Monterrubio Velasco, Marisol, Callaghan, Scott, Modesto Galende, David, Carrasco Jiménez, José Carlos, Badia Sala, Rosa Maria, Pallarés Font de Mora, Pablo, Vázquez Novoa, Fernando, Quintana Ortí, Enrique Salvador, Pienkowska, Marta, and de la Puente, Josep

Abstract

After large-magnitude earthquakes, a crucial task for impact assessment is to rapidly and accurately estimate the ground shaking in the affected region. To satisfy real-time constraints, intensity measures are traditionally evaluated with empirical Ground Motion Models that can drastically limit the accuracy of the estimated values. As an alternative, here we present Machine Learning strategies trained on physics-based simulations that require similar evaluation times. We trained and validated the proposed Machine Learning-based Estimator for ground shaking maps with one of the largest existing datasets (<100M simulated seismograms) from CyberShake developed by the Southern California Earthquake Center covering the Los Angeles basin. For a well-tailored synthetic database, our predictions outperform empirical Ground Motion Models provided that the events considered are compatible with the training data. Using the proposed strategy we show significant error reductions not only for synthetic, but also for five real historical earthquakes, relative to empirical Ground Motion Models., This work has been funded by the European Commission’s Horizon 2020 Framework program and the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 955558 and by MCIN/AEI/ 10.13039/501100011033 and the European Union NextGeneration EU/PRTR(PCI2021-121957), project eFlows4HPC. This research has been supported by the European High-Performance Computing Joint Undertaking (JU) as well as Spain, Italy, Iceland, Germany, Norway, France, Finland, and Croatia under grant agreement no. 101093038, (ChEESE-CoE). The authors acknowledge the Center for Advanced Research Computing (CARC) at the University of Southern California for providing computing resources that have contributed to the research results reported within this publication. URL: https://carc.usc.edu. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725, and the National Center for Supercomputer Applications under the Blue Waters Sustained-Petascale Computing Project. SCEC is funded by USGS Cooperative Agreement G17AC00047 and NSF Cooperative Agreement EAR1600087., Peer Reviewed, Postprint (published version)

Published
2024

12. Enabling dynamic and intelligent workflows for HPC, data analytics, and AI convergence

Author

Ejarque, Jorge, primary, Badia, Rosa M., additional, Albertin, Loïc, additional, Aloisio, Giovanni, additional, Baglione, Enrico, additional, Becerra, Yolanda, additional, Boschert, Stefan, additional, Berlin, Julian R., additional, D’Anca, Alessandro, additional, Elia, Donatello, additional, Exertier, François, additional, Fiore, Sandro, additional, Flich, José, additional, Folch, Arnau, additional, Gibbons, Steven J., additional, Koldunov, Nikolay, additional, Lordan, Francesc, additional, Lorito, Stefano, additional, Løvholt, Finn, additional, Macías, Jorge, additional, Marozzo, Fabrizio, additional, Michelini, Alberto, additional, Monterrubio-Velasco, Marisol, additional, Pienkowska, Marta, additional, de la Puente, Josep, additional, Queralt, Anna, additional, Quintana-Ortí, Enrique S., additional, Rodríguez, Juan E., additional, Romano, Fabrizio, additional, Rossi, Riccardo, additional, Rybicki, Jedrzej, additional, Kupczyk, Miroslaw, additional, Selva, Jacopo, additional, Talia, Domenico, additional, Tonini, Roberto, additional, Trunfio, Paolo, additional, and Volpe, Manuela, additional

Published
2022
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18. High-frequency global wavefields for local 3-D structures by wavefield injection and extrapolation.

Author

Pienkowska, Marta, Monteiller, Vadim, and Nissen-Meyer, Tarje

Subjects
*GREEN'S functions, *SPECTRAL element method, *SYMMETRIC domains, *THEORY of wave motion, *EXTRAPOLATION
Abstract

Earth structure is multiscale, and seismology remains the primary means of deciphering signatures from small structures over large distances. To enable this at the highest resolution, we present a flexible injection and extrapolation type hybrid framework that couples wavefields from a pre-computed global database of accurate Green's functions for 1-D models with a local 3-D method of choice (e.g. a spectral element or a finite-difference solver). The interface allows to embed a full 3-D domain in a spherically symmetric Earth model, tackling large-scale wave propagation with focus on localized heterogeneous complex structures. Thanks to reasonable computational costs (10k CPU hours) and storage requirements (a few TB for 1 Hz waveforms) of databases of global Green's functions, the method provides coupling of 3-D wavefields that can reach the highest observable body-wave frequencies in the 1–4 Hz range. The framework is highly flexible and adaptable; alterations in source properties (radiation patterns and source-time function), in the source–receiver geometry, and in local domain dimensions and location can be introduced without re-running the global simulation. The once-and-for-all database approach reduces the overall computational cost by a factor of 5000–100 000 relative to a full 3-D run, provided that the local domain is of the order of tens of wavelengths in size. In this paper, we present the details of the method and its implementation, show benchmarks with a 3-D spectral element solver, discuss its setup-dependent performance and explore possible wave-propagation applications. [ABSTRACT FROM AUTHOR]

Published
2021
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20. Enabling dynamic and intelligent workflows for HPC, data analytics, and AI convergence

Author

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, Marta Pienkowska, Josep de la Puente, Anna Queralt, Enrique S. Quintana-Ortí, Juan E. Rodríguez, Fabrizio Romano, Riccardo Rossi, Jedrzej Rybicki, Miroslaw Kupczyk, Jacopo Selva, Domenico Talia, Roberto Tonini, Paolo Trunfio, Manuela Volpe, Ejarque, Jorge, Badia, Rosa M., Albertin, Loïc, Aloisio, Giovanni, Baglione, Enrico, Becerra, Yolanda, Boschert, Stefan, Berlin, Julian R., D'Anca, Alessandro, Elia, Donatello, Exertier, Fran(c(c))oi, Fiore, Sandro, Flich, Jos('(e)), Folch, Arnau, Gibbons, Steven J., Koldunov, Nikolay, Lordan, Francesc, Lorito, Stefano, L(o)vholt, Finn, Mac('(i))as, Jorge, Marozzo, Fabrizio, Michelini, Alberto, Monterrubio-Velasco, Marisol, Pienkowska, Marta, de la Puente, Josep, Queralt, Anna, Quintana-Ort('(i)), Enrique S., Rodr('(i))guez, Juan E., Romano, Fabrizio, Rossi, Riccardo, Rybicki, Jedrzej, Kupczyk, Miroslaw, Selva, Jacopo, Talia, Domenico, Tonini, Roberto, Trunfio, Paolo, Volpe, Manuela, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació, Universitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental, Barcelona Supercomputing Center, Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions, Universitat Politècnica de Catalunya. inSSIDE - integrated Software, Service, Information and Data Engineering, Universitat Politècnica de Catalunya. RMEE - Grup de Resistència de Materials i Estructures en l'Enginyeria, European Commission, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), Federal Ministry of Education and Research (Germany), Ministero dello Sviluppo Economico, Norwegian Research Council, Generalitat de Catalunya, and Ministerio de Ciencia e Innovación (España)

Subjects
FOS: Computer and information sciences, Workflow development, Computer Networks and Communications, HPC-DA-AI convergence, Parallel programming, Parallel programming (Computer science), Programació en paral·lel (Informàtica), Workflow orchestration, Distributed computing, High performance computing, Computer Science - Distributed, Parallel, and Cluster Computing, Hardware and Architecture, Distributed, Parallel, and Cluster Computing (cs.DC), Electronic data processing -- Distributed processing, ddc:004, Informàtica::Arquitectura de computadors [Àrees temàtiques de la UPC], Càlcul intensiu (Informàtica), Software, Processament distribuït de dades
Abstract

The evolution of High-Performance Computing (HPC) platforms enables the design and execution of progressively larger and more complex workflow applications in these systems. The complexity comes not only from the number of elements that compose the workflows but also from the type of computations they perform. While traditional HPC workflows target simulations and modelling of physical phenomena, current needs require in addition data analytics (DA) and artificial intelligence (AI) tasks. However, the development of these workflows is hampered by the lack of proper programming models and environments that support the integration of HPC, DA, and AI, as well as the lack of tools to easily deploy and execute the workflows in HPC systems. To progress in this direction, this paper presents use cases where complex workflows are required and investigates the main issues to be addressed for the HPC/DA/AI convergence. Based on this study, the paper identifies the challenges of a new workflow platform to manage complex workflows. Finally, it proposes a development approach for such a workflow platform addressing these challenges in two directions: first, by defining a software stack that provides the functionalities to manage these complex workflows; and second, by proposing the HPC Workflow as a Service (HPCWaaS) paradigm, which leverages the software stack to facilitate the reusability of complex workflows in federated HPC infrastructures. Proposals presented in this work are subject to study and development as part of the EuroHPC eFlows4HPC project., This work has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 955558. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Germany, France, Italy, Poland, Switzerland and Norway. In Spain, it has received complementary funding from MCIN/AEI/10.13039/501100011033, Spain and the European Union NextGenerationEU/PRTR (contracts PCI2021-121957, PCI2021-121931, PCI2021-121944, and PCI2021-121927). In Germany, it has received complementary funding from the German Federal Ministry of Education and Research (contracts 16HPC016K, 6GPC016K, 16HPC017 and 16HPC018). In France, it has received financial support from Caisse des dépôts et consignations (CDC) under the action PIA ADEIP (project Calculateurs). In Italy, it has been preliminary approved for complimentary funding by Ministero dello Sviluppo Economico (MiSE) (ref. project prop. 2659). In Norway, it has received complementary funding from the Norwegian Research Council, Norway under project number 323825. In Switzerland, it has been preliminary approved for complimentary funding by the State Secretariat for Education, Research, and Innovation (SERI), Norway. In Poland, it is partially supported by the National Centre for Research and Development under decision DWM/EuroHPCJU/4/2021. The authors also acknowledge financial support by MCIN/AEI /10.13039/501100011033, Spain through the “Severo Ochoa Programme for Centres of Excellence in R&D” under Grant CEX2018-000797-S, the Spanish Government, Spain (contract PID2019-107255 GB) and by Generalitat de Catalunya, Spain (contract 2017-SGR-01414). Anna Queralt is a Serra Húnter Fellow., With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2018-000797-S).

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