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955 results on '"Valduriez, Patrick"'

151. Efficient Processing of Continuous Join Queries Using Distributed Hash Tables

Author

Palma, Wenceslao, Akbarinia, Reza, Pacitti, Esther, Valduriez, Patrick, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Luque, Emilio, editor, Margalef, Tomàs, editor, and Benítez, Domingo, editor

Published
2008
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155. A Data Allocation Method for Efficient Content-Based Retrieval in Parallel Multimedia Databases

Author

Manjarrez-Sanchez, Jorge, Martinez, José, Valduriez, Patrick, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Thulasiraman, Parimala, editor, He, Xubin, editor, Xu, Tony Li, editor, Denko, Mieso K., editor, Thulasiram, Ruppa K., editor, and Yang, Laurence T., editor

Published
2007
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156. Semantic Query Routing in SenPeer, a P2P Data Management System

Author

Faye, David, Nachouki, Gilles, Valduriez, Patrick, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Enokido, Tomoya, editor, Barolli, Leonard, editor, and Takizawa, Makoto, editor

Published
2007
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157. On the Usage of Clustering for Content Based Image Retrieval

Author

Manjarrez Sanchez, Jorge R., Martinez, Jose, Valduriez, Patrick, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Diekert, Volker, editor, Volkov, Mikhail V., editor, and Voronkov, Andrei, editor

Published
2007
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158. Processing Top-k Queries in Distributed Hash Tables

Author

Akbarinia, Reza, Pacitti, Esther, Valduriez, Patrick, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Kermarrec, Anne-Marie, editor, Bougé, Luc, editor, and Priol, Thierry, editor

Published
2007
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159. Design of PeerSum: A Summary Service for P2P Applications

Author

Hayek, Rabab, Raschia, Guillaume, Valduriez, Patrick, Mouaddib, Noureddine, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Rangan, C. Pandu, editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Cérin, Christophe, editor, and Li, Kuan-Ching, editor

Published
2007
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160. K n Best - A Balanced Request Allocation Method for Distributed Information Systems

Author

Quiané-Ruiz, Jorge-Arnulfo, Lamarre, Philippe, Valduriez, Patrick, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Kotagiri, Ramamohanarao, editor, Krishna, P. Radha, editor, Mohania, Mukesh, editor, and Nantajeewarawat, Ekawit, editor

Published
2007
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161. Top-k Query Processing in the APPA P2P System

Author

Akbarinia, Reza, Martins, Vidal, Pacitti, Esther, Valduriez, Patrick, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Rangan, C. Pandu, editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Daydé, Michel, editor, Palma, José M. L. M., editor, Coutinho, Álvaro L. G. A., editor, Pacitti, Esther, editor, and Lopes, João Correia, editor

Published
2007
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162. Query Expansion and Interpretation to Go Beyond Semantic P2P Interoperability

Author

Ventresque, Anthony, Cazalens, Sylvie, Lamarre, Philippe, Valduriez, Patrick, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Meersman, Robert, editor, and Tari, Zahir, editor

Published
2007
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163. Satisfaction-Based Query Load Balancing

Author

Quiané-Ruiz, Jorge-Arnulfo, Lamarre, Philippe, Valduriez, Patrick, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Meersman, Robert, editor, and Tari, Zahir, editor

Published
2006
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164. Model-Driven Tool Interoperability: An Application in Bug Tracking

Author

Del Fabro, Marcos Didonet, Bézivin, Jean, Valduriez, Patrick, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Meersman, Robert, editor, and Tari, Zahir, editor

Published
2006
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165. Apuama: Combining Intra-query and Inter-query Parallelism in a Database Cluster

Author

Miranda, Bernardo, Lima, Alexandre A. B., Valduriez, Patrick, Mattoso, Marta, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Grust, Torsten, editor, Höpfner, Hagen, editor, Illarramendi, Arantza, editor, Jablonski, Stefan, editor, Mesiti, Marco, editor, Müller, Sascha, editor, Patranjan, Paula-Lavinia, editor, Sattler, Kai-Uwe, editor, Spiliopoulou, Myra, editor, and Wijsen, Jef, editor

Published
2006
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166. Integrating Machine Learning Model Ensembles to the SAVIME Database System

Author

Silva, Anderson, Valduriez, Patrick, Machado Porto, Fábio André, Laboratorio Nacional de Computação Cientifica [Rio de Janeiro] (LNCC / MCT), Scientific Data Management (ZENITH), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Sociedade Brazileira de Computação (SBC), équipe associée Inria hpdasc, and European Project: 101016478,RISC2

Subjects
Machine Learning, Data Management Systems, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Database systems, Machine learning models, Ensemble Classifier, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

XXXVII WORKSHOP BRASILEIRO DE INTEGRAÇÃO DE BANCOS DE DADOS E INTELIGÊNCIA ARTIFICIAL; International audience; The integration of machine learning algorithms into database systems has brought new opportunities in different areas from indexing to query optimization. In this paper, we describe the integration of an approach for the automatic computation of model ensembles to answer a predictive query. We have extended the SAVIME multi-dimensional array DBMS by adding a new function to its query language and implementing the selection and allocation ensemble model dataflow into the query processing component of SAVIME. We show some initial experimental results depicting its performance against a pure Python implementation of the ensemble approach. Interestingly enough the C++ implementation within SAVIME is up to 4 times faster than its competitor.

Published
2022
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168. Scaling Up the Preventive Replication of Autonomous Databases in Cluster Systems

Author

Coulon, Cédric, Pacitti, Esther, Valduriez, Patrick, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Daydé, Michel, editor, Dongarra, Jack, editor, Hernández, Vicente, editor, and Palma, José M. L. M., editor

Published
2005
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169. Data Management in Large-Scale P2P Systems

Author

Valduriez, Patrick, Pacitti, Esther, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Daydé, Michel, editor, Dongarra, Jack, editor, Hernández, Vicente, editor, and Palma, José M. L. M., editor

Published
2005
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170. Modeling in the Large and Modeling in the Small

Author

Bézivin, Jean, Jouault, Frédéric, Rosenthal, Peter, Valduriez, Patrick, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Aßmann, Uwe, editor, Aksit, Mehmet, editor, and Rensink, Arend, editor

Published
2005
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171. Capturing Provenance to Improve the Model Training of PINNs: first handon experiences with Grid5000

Author

Silva, Rômulo, Pina, Débora, Kunstmann, Liliane, de Oliveira, Daniel, Valduriez, Patrick, Coutinho, Alvaro, Mattoso, Marta, Universidade Federal do Rio de Janeiro (UFRJ), Universidade Federal Fluminense [Rio de Janeiro] (UFF), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), IACM, Associated team Hpdasc, Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), and Valduriez, Patrick

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Eikonal Equation, Hybrid Computing, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Provenance, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], Physics-informed Neural Networks, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; The growing popularity of Neural Networks in computational science and engineering raises several challenges in configuring training parameters and validating the models. Machine learning has been used to approximate costly computational problems in computational mechanics, discover equations by coefficient estimation, and build surrogates. Those applications are outside of the common usage of neural networks. They require a different set of techniques generally encompassed by Physics-Informed Neural Networks (PINNs), which appear to be a good alternative for solving forward and inverse problems governed by PDEs in a small data regime, especially when it comes to Uncertainty Quantification. PINNs have been successfully applied for solving problems in fluid dynamics, inference of hydraulic conductivity, velocity inversion, phase separation, and many others. Nevertheless, we still need to investigate its computational aspects, especially its scalability, when running in large-scale systems. Several hyperparameter configurations have to be evaluated to reach a trained model, often requiring fine-tuning hyperparameters, despite the existence of a few setting recommendations. In PINNs, this fine-tuning requires analyzing configurations and how they relate to the loss function evaluation. We propose provenance data capture and analysis techniques to improve the model training of PINNs. We also report our first experiences on running PINNs in Grid5000 using hybrid CPU-GPU computing.

Published
2021

175. Refresco: Improving Query Performance Through Freshness Control in a Database Cluster

Author

Le Pape, Cécile, Gançarski, Stéphane, Valduriez, Patrick, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Meersman, Robert, editor, and Tari, Zahir, editor

Published
2004
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176. A Flexible Mediation Process for Large Distributed Information Systems

Author

Lamarre, Philippe, Cazalens, Sylvie, Lemp, Sandra, Valduriez, Patrick, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Meersman, Robert, editor, and Tari, Zahir, editor

Published
2004
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177. OLAP Query Processing in a Database Cluster

Author

Lima, Alexandre A. B., Mattoso, Marta, Valduriez, Patrick, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Danelutto, Marco, editor, Vanneschi, Marco, editor, and Laforenza, Domenico, editor

Published
2004
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179. Locking in OODBMS Clients Supporting Nested Transactions

Author

Daynès, Laurent, Gruber, Olivier, Valduriez, Patrick, Aigrain, P., editor, Aldana, F., editor, Danielmeyer, H. G., editor, Faugeras, O., editor, Gallaire, H., editor, Kowalski, R. A., editor, Lehn, J. M., editor, Levi, G., editor, Metakides, G., editor, Oakley, B., editor, Rasmussen, J., editor, Tribolet, J., editor, Tsichritzis, D., editor, Van Overstraeten, R., editor, Wrixon, G., editor, Atkinson, Malcolm P., editor, and Welland, Ray, editor

Published
2000
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190. Adaptive parallel query execution in DBS3

Author

Bouganim, Luc, Dageville, Benoît, Valduriez, Patrick, Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Apers, Peter, editor, Bouzeghoub, Mokrane, editor, and Gardarin, Georges, editor

Published
1996
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197. SUQ$2$: Uncertainty Quantification Queries over Large Spatio-temporal Simulations

Author

Lemus, Noel, Porto, Fábio, Souto, Yania, Pereira, Rafael, Liu, Ji, Pacitti, Esther, Valduriez, Patrick, Laboratorio Nacional de Computação Cientifica [Rio de Janeiro] (LNCC / MCT), Baidu Research, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Inria associated team SciDISC, Inria associated team HPDaSc, European Project: 689772,H2020 Pilier Industrial Leadership,H2020-EUB-2015,HPC4E(2015), Valduriez, Patrick, HPC for Energy - HPC4E - - H2020 Pilier Industrial Leadership2015-12-01 - 2017-11-30 - 689772 - VALID, and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], Numerical simulation, Queries, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Uncertainty quantification
Abstract

International audience; The combination of high-performance computing towards Exascale power and numerical techniques enables exploring complex physical phenomena using large-scale spatio-temporal modeling and simulation. The improvements on the fidelity of phenomena simulation require more sophisticated uncertainty quantification analysis, leaving behind measurements restricted to low order statistical moments and moving towards more expressive probability density functions models of uncertainty. In this paper, we consider the problem of answering uncertainty quantification queries over large spatio-temporal simulation results. We propose the SU Q 2 method based on the Generalized Lambda Distribution (GLD) function. GLD fitting is an embarrassingly parallel process that scales linearly to the number of available cores on the number of simulation points. Furthermore, the answer of queries is entirely based on computed GLDs and the corresponding clusters, which enables trading the huge amount of simulation output data by 4 values in the GLD parametrization per simulation point. The methodology presented in this paper becomes an important ingredient in converging simulations improvements to the Exascale computational power.

Published
2020

200. Towards Integrated Hardware/Software Ecosystems for the Edge-Cloud-HPC Continuum

Author

Antoniu, Gabriel, Valduriez, Patrick, Hoppe, Hans-Christian, and Krüger, Jens

Abstract

Modern use cases such as autonomous vehicles, digital twins, smart buildings and precision agriculture, greatly increase the complexity of application workflows. They typically combine physics-based simulations, analysis of large data volumes and machine learning and require a hybrid execution infrastructure: edge devices create streams of input data, which are processed by data analytics and machine learning applications in the Cloud, and simulations on large, specialised HPC systems provide insights into and prediction of future system state. From these results, additional steps create and communicate output data across the infrastructure levels, and for some use cases, control devices or cyber-physical systems in the real world are controlled (as in the case of smart factories). All of these steps pose different requirements for the best suited execution platforms, and they need to be connected in an efficient and secure way. This assembly is called the Computing Continuum (CC) (1). It raises challenges at multiple levels: at the application level, innovative algorithms are needed to bridge simulations, machine learning and data-driven analytics; at the middleware level, adequate tools must enable efficient deployment, scheduling and orchestration of the workflow components across the whole distributed infrastructure; and, finally, a capable resource management system must allocate a suitable set of components of the infrastructure to run the application workflow, preferably in a dynamic and adaptive way, taking into account the specific capabilities of each component of the underlying heterogeneous infrastructure. To address the challenges, we foresee an increasing need for integrated software ecosystems which combine current “island” solutions and bridge the gaps between them. These ecosystems must facilitate the full lifecycle of CC use cases, including initial modelling, programming, deployment, execution, optimisation, as well as monitoring and control. It will be important to ensure adequate reproducibility of workflow results and to find ways for creating and managing trust when sharing systems, software and data. All of these will in turn require novel or improved hardware capabilities. This white paper provides an initial discussion of the gaps. Our objective is to accelerate progress in both hardware and software infrastructures to build CC use cases, with the ultimate goals of accelerating scientific discovery, improving timeliness, quality and sustainability of engineering artefacts, and supporting decisions in complex and potentially urgent situations, The authors would like to thank Rafael Mayo-García from CIEMAT and Marion Carrier from CybeleTech for their help in describing relevant use cases for the computing continuum., {"references":["D. Balouek-Thomert, E. Gibert Renart , A. R. Zamani, A. Simonet and M. Parashar, \"Towards a computing continuum: Enabling edge-to-cloud integration for data-driven workflows,\" The International Journal of High Performance Computing Applications, vol. 33, no. 6, 2019.","G. Nguyen, S. Dlugolinsky , M. Bobák, V. Tran, A. López García, I. Heredia, P. Malík and L. Hluchý, \"Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey,\" Artificial Intelligence Review, vol. 52, p. 77–124, 2019.","O. Peckham, \"15 Years Later, the Green500 Continues Its Push for Energy Efficiency as a First-Order Concern in HPC,\" HPCwire, no. https://www.hpcwire.com/2021/07/15/15-years-later-the-green500-continues-its-push-for-energy-efficiency-as-a-first-order-concern-in-hpc/, 17 07 2021.","E. Masamet, A. Shehabi, N. Lei, S. Smith and J. Koomey, \"Recalibrating global data center energy use estimates,\" Science, vol. 367, no. 6481, pp. 984-986, 2020.","Hyperion Research, [Online]. Available: https://hyperionresearch.com/wp-content/uploads/2021/07/Hyperion-Research-HPC-Briefing-Slides-During-ISC-June-2021.b.pdf.","S. Dewitte, J. P. Cornelis, R. Müller and A. Munteanu, \"Artificial Intelligence Revolutionises Weather Forecast, Climate Monitoring and Decadal Prediction,\" Remote Sensing, vol. 13, no. 16, p. 3209, 2021.","M. Feldman, \"HPC in 2020: AI is no longer an experiment,\" The Next Platform, no. https://www.nextplatform.com/2020/01/09/hpc-in-2020-ai-is-no-longer-an-experiment/, 09 01 2020.","Google Cloud, \"Helping researchers at CERN to analyze powerful data and uncover the secrets of our universe,\" [Online]. Available: https://cloud.google.com/customers/cern.","O. Peckham, \"Ahead of 'Dojo,' Tesla Reveals Its Massive Precursor Supercomputer,\" HPCwire, no. https://www.hpcwire.com/2021/06/22/ahead-of-dojo-tesla-reveals-its-massive-precursor-supercomputer/, 22 06 2021.","K. Fauvel, D. Balouek-Thomert, D. Melgar, P. Silva, A. Simonet, G. Antoniu, A. Costan, V. Masson, M. Parashar, I. Rodero and A. Termier, \"A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning,\" in 34th AAAI Conference on Artificial Intelligence, Outstanding Paper Award - Special Track for Social Impact, New York, https://ojs.aaai.org//index.php/AAAI/article/view/5376, 2020.","M. Asch, F. Bodin, M. Beck, T. Moore, M. Taufer, M. Swany and J.-P. Vilotte, \"Cybercosm: New Foundations for a Converged Science Data Ecosystem,\" https://arxiv.org/abs/2105.10680v3, 2021."]}

Published
2021
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