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Giorgio Amati

dblp:266/0294 · DBLP profile ↗
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3ranked-venue papers
0as first author
3since 2021 · last 2026
0000-0003-1116-1443ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 3 · 3 since 2021
YearPublicationVenuePosition
2026 miniLB: Benchmarking Lattice Boltzmann simulations on AMD, Intel, and NVIDIA GPUs
abstract
In computational fluid dynamics, the Lattice Boltzmann method is a computational technique that has gained popularity due to its flexibility in handling complex geometries and turbulence models, and its unique suitability for massive parallel processing. The method, which discretizes both space and velocity into a lattice structure, has been the subject of highly sophisticated implementation by academia and industry, resulting in very large and engineered code bases. This article introduces miniLB , to the best of our knowledge the first SYCL-based mini-application for the Lattice Boltzmann method. Thanks to its minimalist structure, miniLB is a perfect benchmark to address four key aspects of lattice Boltzmann implementations: (a) GPU acceleration, thanks to an efficient implementation in SYCL capable of abstracting complex fluid dynamics simulations across heterogeneous computing systems; (b) performance portability, with an efficient mapping to SYCL semantics focused on performance portability, evaluated on GPUs from different vendors; (c) mixed precision, with four different variations exploiting combinations of double, single, and half floating-point representations; (d) flexibility, demonstrated through four different use cases, including Lid-driven cavity, Von Karmann street, Poiseuille flow, and Taylor–Green vortex. The results of miniLB , compared to a manually tuned FORTRAN version, demonstrate the effectiveness of miniLB in assessing performance portability across different hardware, while providing valuable insights for optimizing large-scale lattice Boltzmann simulations in modern massively parallel computing systems.
Biagio Cosenza, Luigi Crisci, Giorgio Amati, Matteo Turisini
 Future Gener. Comput. Syst.3
2024 On floating point precision in computational fluid dynamics using OpenFOAM
abstract
Thanks to the computational power of modern cluster machines, numerical simulations can provide, with an unprecedented level of details, new insights into fluid mechanics. However, taking full advantage of this hardware remains challenging since data communication remains a significant bottleneck to reaching peak performances. Reducing floating point precision is a simple and effective way to reduce data movement and improve the computational speed of most applications. Nevertheless, special care needs to be taken to ensure the quality and convergence of computed solutions, especially when dealing with complex fluid simulations. In this work, we analyse the impact of reduced (single and mixed compared to double) precision on computational performance and accuracy for computational fluid dynamics. Using the open source library OpenFOAM, we consider incompressible, compressible, and multiphase fluid solvers for testing on relevant benchmarks for flows in the laminar and turbulent regime and in the presence of shock waves. Computational gain and changes in the scalability of applications in reduced precision are also discussed. In particular, an ad hoc theoretical model for the strong scaling allows us to interpret and understand the observed behaviors, as a function of floating point precision and hardware specifics. Finally, we show how reduced precision can significantly speed up a hybrid CPU–GPU implementation, made available to OpenFOAM end-users recently, that simply relies on a GPU linear algebra solver developed by hardware vendors.
Federico Brogi, Simone Bnà, Gabriele Boga, Giorgio Amati, Tomaso Esposti Ongaro, Matteo Cerminara
 Future Gener. Comput. Syst.4
2023 The EU Center of Excellence for Exascale in Solid Earth (ChEESE): Implementation, results, and roadmap for the second phase
abstract
The EU Center of Excellence for Exascale in Solid Earth (ChEESE) develops exascale transition capabilities in the domain of Solid Earth, an area of geophysics rich in computational challenges embracing different approaches to exascale (capability, capacity, and urgent computing). The first implementation phase of the project (ChEESE-1P; 2018–2022) addressed scientific and technical computational challenges in seismology, tsunami science, volcanology, and magnetohydrodynamics, in order to understand the phenomena, anticipate the impact of natural disasters, and contribute to risk management. The project initiated the optimisation of 10 community flagship codes for the upcoming exascale systems and implemented 12 Pilot Demonstrators that combine the flagship codes with dedicated workflows in order to address the underlying capability and capacity computational challenges. Pilot Demonstrators reaching more mature Technology Readiness Levels (TRLs) were further enabled in operational service environments on critical aspects of geohazards such as long-term and short-term probabilistic hazard assessment, urgent computing, and early warning and probabilistic forecasting. Partnership and service co-design with members of the project Industry and User Board (IUB) leveraged the uptake of results across multiple research institutions, academia, industry, and public governance bodies (e.g. civil protection agencies). This article summarises the implementation strategy and the results from ChEESE-1P, outlining also the underpinning concepts and the roadmap for the on-going second project implementation phase (ChEESE-2P; 2023–2026).
Arnau Folch, Claudia Abril, Michael Afanasiev, Giorgio Amati, Michael Bader, Rosa M. Badia, Hafize B. Bayraktar, Sara Barsotti, Roberto Basili 0002, Fabrizio Bernardi, Christian Boehm, Beatriz Brizuela, Federico Brogi, Eduardo Cabrera, Emanuele Casarotti, Manuel Jesús Castro Díaz, Matteo Cerminara, Antonella Cirella, Alexey Cheptsov, Javier Conejero, Antonio Costa 0002, Marc de la Asunción, Josep de la Puente, Marco Djuric, Ravil Dorozhinskii, Gabriela Espinosa, Tomaso Esposti Ongaro, Joan Farnós, Nathalie Favretto-Cristini, Andreas Fichtner, Alexandre Fournier, Alice-Agnes Gabriel, Jean-Matthieu Gallard, Steven J. Gibbons, Sylfest Glimsdal, José Manuel González-Vida, José Gracia, Rose Gregorio, Natalia Gutiérrez, Benedikt Halldorsson, Okba Hamitou, Guillaume Houzeaux, Stephan Jaure, Mouloud Kessar, Lukas Krenz, Lion Krischer, Soline Laforet, Piero Lanucara, Bo Li 0147, Maria Concetta Lorenzino, Stefano Lorito, Finn Løvholt, Giovanni Macedonio, Jorge Macías Sánchez, Guillermo Marin, Beatriz Martínez Montesinos, Leonardo Mingari, Geneviève Moguilny, Vadim Montellier, Marisol Monterrubio Velasco, Georges-Emmanuel Moulard, Masaru Nagaso, Massimo Nazaria, Christoph Niethammer, Federica Pardini, Marta Pienkowska, Luca Pizzimenti, Natalia Poiata, Leonhard Rannabauer, Otilio Rojas, Juan Esteban Rodriguez, Fabrizio Romano, Oleksandr Rudyy, Vittorio Ruggiero, Philipp Samfass, Carlos Sánchez-Linares, Sabrina Sanchez, Laura Sandri, Antonio Scala, Nathanaël Schaeffer, Joseph Schuchart, Jacopo Selva, Amadine Sergeant, Angela Stallone, Matteo Taroni, Solvi Thrastarson, Manuel Titos, Nadia Tonelllo, Roberto Tonini, Thomas Ulrich, Jean-Pierre Vilotte, Malte Vöge, Manuela Volpe, Sara Aniko Wirp, Uwe Wössner
 Future Gener. Comput. Syst.4