http://www.cnr.it/ontology/cnr/individuo/prodotto/ID271998
A Multi-GPU Implementation of a D2Q37 Lattice Boltzmann Code (Contributo in atti di convegno)
- Type
- Label
- A Multi-GPU Implementation of a D2Q37 Lattice Boltzmann Code (Contributo in atti di convegno) (literal)
- Anno
- 2012-01-01T00:00:00+01:00 (literal)
- Alternative label
Biferale, Luca and Mantovani, Filippo and Pivanti, Marcello and Pozzati, Fabio and Sbragaglia, Mauro and Scagliarini, Andrea and Schifano, Sebastiano Fabio and Toschi, Federico and Tripiccione, Raffaele (2012)
A Multi-GPU Implementation of a D2Q37 Lattice Boltzmann Code
in 9th International Conference on Parallel Processing and Applied Mathematics (PPAM), Torun, POLAND, SEP 11-14, 2011, Torun, POLAND, SEP 11-14, 2011
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Biferale, Luca and Mantovani, Filippo and Pivanti, Marcello and Pozzati, Fabio and Sbragaglia, Mauro and Scagliarini, Andrea and Schifano, Sebastiano Fabio and Toschi, Federico and Tripiccione, Raffaele (literal)
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
- 9th International Conference on Parallel Processing and Applied Mathematics (PPAM), Torun, POLAND, SEP 11-14, 2011 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
- PARALLEL PROCESSING AND APPLIED MATHEMATICS, PT I (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Note
- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Biferale, L (Reprint Author), Univ Roma Tor Vergata, Rome, Italy. Biferale, Luca; Sbragaglia, Mauro, Univ Roma Tor Vergata, Rome, Italy. (literal)
- Titolo
- A Multi-GPU Implementation of a D2Q37 Lattice Boltzmann Code (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
- 978-3-642-31463-6 (literal)
- Abstract
- We describe a parallel implementation of a compressible Lattice Boltzmann code on a multi-GPU cluster based on Nvidia Fermi processors. We analyze how to optimize the algorithm for GP-GPU architectures, describe the implementation choices that we have adopted and compare our performance results with an implementation optimized for latest generation multi-core CPUs. Our program runs at approximate to 30% of the double-precision peak performance of one GPU and shows almost linear scaling when run on the multi-GPU cluster. (literal)
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