Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Molto, German"'
Autor:
Sala, Roberto, Guindani, Bruno, Galimberti, Enrico, Filippini, Federica, Sedghani, Hamta, Ardagna, Danilo, Risco, Sebastián, Moltó, Germán, Caballer, Miguel
This paper proposes an automated framework for efficient application profiling and training of Machine Learning (ML) performance models, composed of two parts: OSCAR-P and aMLLibrary. OSCAR-P is an auto-profiling tool designed to automatically test s
Externí odkaz:
http://arxiv.org/abs/2411.07687
Publikováno v:
In Future Generation Computer Systems December 2024 161:174-188
Autor:
Bernardo, Samuel, Orviz, Pablo, David, Mario, Gomes, Jorge, Arce, David, Naranjo, Diana, Blanquer, Ignacio, Campos, Isabel, Moltó, Germán, Pina, Joao
Publikováno v:
In Future Generation Computer Systems July 2024 156:254-268
Publikováno v:
In Future Generation Computer Systems April 2024 153:457-466
Publikováno v:
J Grid Computing 19, 4 (2021)
Virtual clusters are widely used computing platforms than can be deployed in multiple cloud platforms. The ability to dynamically grow and shrink the number of nodes has paved the way for customised elastic computing both for High Performance Computi
Externí odkaz:
http://arxiv.org/abs/2102.08710
Autor:
Sala, Roberto, Guindani, Bruno, Galimberti, Enrico, Filippini, Federica, Sedghani, Hamta, Ardagna, Danilo, Risco, Sebastián, Moltó, Germán, Caballer, Miguel
Publikováno v:
In The Journal of Systems & Software March 2025 221
Akademický článek
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Akademický článek
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Autor:
Caballer, Miguel, Zala, Sahdev, García, Álvaro López, Moltó, Germán, Fernández, Pablo Orviz, Velten, Mathieu
Publikováno v:
J Grid Computing (2017)
Private cloud infrastructures are now widely deployed and adopted across technology industries and research institutions. Although cloud computing has emerged as a reality, it is now known that a single cloud provider cannot fully satisfy complex use
Externí odkaz:
http://arxiv.org/abs/1711.03334
Autor:
Collaboration, INDIGO-DataCloud, Salomoni, Davide, Campos, Isabel, Gaido, Luciano, de Lucas, Jesus Marco, Solagna, Peter, Gomes, Jorge, Matyska, Ludek, Fuhrman, Patrick, Hardt, Marcus, Donvito, Giacinto, Dutka, Lukasz, Plociennik, Marcin, Barbera, Roberto, Blanquer, Ignacio, Ceccanti, Andrea, David, Mario, Duma, Cristina, López-García, Alvaro, Moltó, Germán, Orviz, Pablo, Sustr, Zdenek, Viljoen, Matthew, Aguilar, Fernando, Alves, Luis, Antonacci, Marica, Antonelli, Lucio Angelo, Bagnasco, Stefano, Bonvin, Alexandre M. J. J., Bruno, Riccardo, Cetinic, Eva, Chen, Yin, Chiarello, Fabrizio, Costa, Alessandro, Pra, Stefano Dal, Davidovic, Davor, Dorigo, Alvise, Ertl, Benjamin, Fanzago, Federica, Fargetta, Marco, Fiore, Sandro, Gallozzi, Stefano, Kurkcuoglu, Zeynep, Lloret, Lara, Martins, Joao, Nuzzo, Alessandra, Nassisi, Paola, Palazzo, Cosimo, Pina, Joao, Sciacca, Eva, Segatta, Matteo, Sgaravatto, Massimo, Spiga, Daniele, Taneja, Sonia, Tangaro, Marco Antonio, Urbaniak, Michal, Vallero, Sara, Verlato, Marco, Wegh, Bas, Zaccolo, Valentina, Zambelli, Federico, Zangrando, Lisa, Zani, Stefano, Zok, Tomasz
This paper describes the achievements of the H2020 project INDIGO-DataCloud. The project has provided e-infrastructures with tools, applications and cloud framework enhancements to manage the demanding requirements of scientific communities, either l
Externí odkaz:
http://arxiv.org/abs/1711.01981