Guillaume Houzeaux

EDBT 2026 Demo / reviewers in the wild / expert

dblp:45/9756 · DBLP profile ↗

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6ranked-venue papers

0as first author

4since 2021 · last 2025

0000-0002-2592-1426ORCID · verified

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

Systems, architecture and hardware · 4 · 3 since 2021Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 1Theory of computation · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1

Year	Publication	Venue	Position
2025	A formalization of parallel data exchange algorithms used by numerical methods for solving partial differential equations Cristóbal Samaniego, Guillaume Houzeaux	Theor. Comput. Sci.	2
2024	Exploiting long vectors with a CFD code: a co-design show case abstract A current trend in HPC systems is the utilization of architectures with SIMD or vector extensions to exploit data parallelism. There are several ways to take advantage of such modern vector architectures, each with a different impact on the code and its portability. For example, the use of intrinsics, guided vectorization via pragmas, or compiler autovectorization. Our objectives are to maximize vectorization efficiency and minimize code specialization. To achieve these objectives, we rely on compiler autovectorization. We leverage a set of hardware and software tools that allow us to analyze in detail where autovectorization is suboptimal. Thus, we apply an iterative methodology that allows us to incrementally improve the efficient use of the underlying hardware. In this paper, we apply this methodology to a CFD production code. We evaluate the performance on an innovative configurable platform powered by a RISC-V core coupled with a wide vector unit capable of operating with up to 256 double precision elements. Following the vectorization process, we demonstrate a single-core speedup of 7.6× compared to its scalar implementation. Furthermore, we show that code portability is not compromised, as our solution continues to exhibit performance benefits, or at the very least, no drawbacks, on other HPC architectures such as Intel x86 and NEC SX-Aurora. Marc Blancafort, Roger Ferrer, Guillaume Houzeaux, Marta Garcia-Gasulla, Filippo Mantovani	IPDPS	3
2024	Alya towards Exascale: Optimal OpenACC Performance of the Navier-Stokes Finite Element Assembly on GPUs abstract This paper addresses the challenge of providing portable and highly efficient code structures for CPU and GPU architectures. We choose the assembly of the right-hand term in the incompressible flow module of the High-Performance Computational Mechanics code Alya, which is one of the two CFD codes in the Unified European Benchmark Suite. Starting from an efficient CPU-code and a related OpenACC-port for GPUs we successively investigate performance potentials arising from code specialization, algorithmic restructuring and low-level optimizations.We demonstrate that only the combination of these different dimensions of runtime optimization unveils the full performance potential on the GPU and CPU. Roofline-based performance modelling is applied in this process and we demonstrate the need to investigate new optimization strategies if a classical roofline limit such as memory bandwidth utilization is achieved, rather than stopping the process. The final unified OpenACC-based implementation boosts performance by more than 50x on an NVIDIA A100 GPU (achieving approximately 2.5 TF/s FP64) and a further factor of 5x for an Intel Icelake based CPU-node (achieving approximately 1.0 TF/s FP64).The insights gained in our manual approach lays ground implementing unified but still highly efficient code structures for related kernels in Alya and other applications. These can be realized by manual coding or automatic code generation frameworks. Herbert Owen, Dominik Ernst, Thomas Gruber 0007, Oriol Lehmkuhl, Guillaume Houzeaux, Lucas Gasparino, Gerhard Wellein	IPDPS	5
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.	42
2020	Heterogeneous CPU/GPU co-execution of CFD simulations on the POWER9 architecture: Application to airplane aerodynamics Ricard Borrell, Damien Dosimont, Marta Garcia-Gasulla, Guillaume Houzeaux, Oriol Lehmkuhl, V. Mehta, Herbert Owen, Mariano Vázquez, Guillermo Oyarzun	Future Gener. Comput. Syst.	4
2019	The OTree: Multidimensional Indexing with efficient data Sampling for HPC abstract Spatial big data is considered an essential trend in future scientific and business applications. Indeed, research instruments, medical devices, and social networks generate hundreds of petabytes of spatial data per year. However, many authors have pointed out that the lack of specialized frameworks for multidimensional Big Data is limiting possible applications and precluding many scientific breakthroughs. Paramount in achieving High-Performance Data Analytics is to optimize and reduce the I/O operations required to analyze large data sets. To do so, we need to organize and index the data according to its multidimensional attributes. At the same time, to enable fast and interactive exploratory analysis, it is vital to generate approximate representations of large datasets efficiently. In this paper, we propose the Outlook Tree (or OTree), a novel Multidimensional Indexing with efficient data Sampling (MIS) algorithm. The OTree enables exploratory analysis of large multidimensional datasets with arbitrary precision, a vital missing feature in current distributed data management solutions. Our algorithm reduces the indexing overhead and achieves high performance even for write-intensive HPC applications. Indeed, we use the OTree to store the scientific results of a study on the efficiency of drug inhalers. Then we compare the OTree implementation on Apache Cassandra, named Qbeast, with PostgreSQL and plain storage. Lastly, we demonstrate that our proposal delivers better performance and scalability. Cesare Cugnasco, Hadrien Calmet, Pol Santamaria, Raül Sirvent, Beatriz Eguzkitza, Guillaume Houzeaux, Yolanda Becerra 0001, Jordi Torres, Jesús Labarta	IEEE BigData	6