EDBT 2026 Demo / reviewers in the wild / expert
Huy-Nam Nguyen
dblp:177/3265
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4ranked-venue papers
0as first author
4since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 4 · 4 since 2021Software engineering, systems software and programming languages · 1 · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Accelerating legacy applications with spatial computing devicesabstractAbstract Heterogeneous computing is the major driving factor in designing new energy-efficient high-performance computing systems. Despite the broad adoption of GPUs and other specialized architectures, the interest in spatial architectures like field-programmable gate arrays (FPGAs) has grown. While combining high performance, low power consumption and high adaptability constitute an advantage, these devices still suffer from a weak software ecosystem, which forces application developers to use tools requiring deep knowledge of the underlying system, often leaving legacy code (e.g., Fortran applications) unsupported. By realizing this, we describe a methodology for porting Fortran (legacy) code on modern FPGA architectures, with the target of preserving performance/power ratios. Aimed as an experience report, we considered an industrial computational fluid dynamics application to demonstrate that our methodology produces synthesizable OpenCL codes targeting Intel Arria10 and Stratix10 devices. Although performance gain is not far beyond that of the original CPU code (we obtained a relative speedup of $$\times$$ × 0.59 and $$\times$$ × 0.63, respectively, for a single optimized main kernel, while only on the Stratix10 we achieved $$\times$$ × 2.56 by replicating the main optimized kernel 4 times), our results are quite encouraging to drawn the path for further investigations. This paper also reports some major criticalities in porting Fortran code on FPGA architectures. Paolo Savio, Alberto Scionti, Giacomo Vitali, Paolo Viviani 0001, Chiara Vercellino, Olivier Terzo, Huy-Nam Nguyen, Donato Magarielli, Ennio Spano, Michele Marconcini, Francesco Poli |
J. Supercomput. | 7 |
| 2022 | EVOLVE: Towards Converging Big-Data, High-Performance and Cloud-Computing WorldsabstractEVOLVE is a pan European Innovation Action that aims to fully-integrate High-Performance-Computing (HPC) hardware with state-of-the-art software technologies under a unique testbed, that enables the convergence of HPC, Cloud and Big-Data worlds and increases our ability to extract value from massive and demanding datasets. EVOLVE's advanced compute platform combines HPC-enabled capabilities, with transparent deployment in high abstraction level, and a versatile Big-Data processing stack for end-to-end workflows. Hence, domain experts have the potential to improve substantially the efficiency of existing services or introduce new models in the respective domains, e.g., automotive services, bus transportation, maritime surveillance and others. In this paper, we describe EVOLVE's testbed, and evaluate the performance of the integrated pilots from different domains. Achilleas Tzenetopoulos, Dimosthenis Masouros, Konstantina Koliogeorgi, Sotirios Xydis, Dimitrios Soudris, Antony Chazapis, Christos Kozanitis, Angelos Bilas, Christian Pinto, Huy-Nam Nguyen, Stelios Louloudakis, Georgios Gardikis, George Vamvakas, Michelle Aubrun, Christi Symeonidou, Vassilis Spitadakis, Konstantinos F. Xylogiannopoulos, Bernhard Peischl, Tahir Emre Kalayci, Alexander Stocker, Jean-Thomas Acquaviva |
DATE | 10 |
| 2021 | EVOLVE: HPC and cloud enhanced testbed for extracting value from large-scale diverse dataabstractEVOLVE is a pan-European Innovation Action building a converged infrastructure to bring together the HPC, Cloud, and Big Data worlds. EVOLVE's platform and software stack supports large-scale, data-intensive applications, driven primarily by industry requirements set by pilot and proof-of-concept use cases from diverse fields. Given the unprecedented data growth we are experiencing, EVOLVE's infrastructure is key in enabling the cost-effective processing of massive amounts of data and the adaptation of multiple high-end technologies, in an environment that fosters interoperability and enforces increased security. Antony Chazapis, Jean-Thomas Acquaviva, Angelos Bilas, Georgios Gardikis, Christos Kozanitis, Stelios Louloudakis, Huy-Nam Nguyen, Christian Pinto, Arno Scharl, Dimitrios Soudris |
CF | 7 |
| 2021 | FPGA acceleration in EVOLVE's Converged Cloud-HPC InfrastructureabstractThe EVOLVE project aims to take important steps in bringing together Big Data, HPC and Cloud domains in a single testbed and expose its services through a user friendly and transparent interface. The EVOLVE testbed is enhanced with acceleration capabilities by leveraging the power of heterogeneous technologies and allows the user to develop and deploy applications through Zeppelin notebooks with ease of use. Konstantina Koliogeorgi, Fekhr Eddine Keddous, Dimosthenis Masouros, Antony Chazapis, Michelle Aubrun, Sotirios Xydis, Angelos Bilas, Romain Hugues, Jean-Thomas Acquaviva, Huy-Nam Nguyen, Dimitrios Soudris |
FPL | 10 |