NET4EXA: Pioneering the Future of Interconnects for Supercomputing and AI

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Authors: Michele Martinelli, Roberto Ammendola, A. Biagioni, Carlotta Chiarini, Ottorino Frezza, Francesca Lo Cicero, A. Lonardo, Pier Stanislao Paolucci, Elena Pastorelli, Pierpaolo Perticaroli, L. Pontisso, Rossi, Cristian
+32 more Francesco Simula, PIERO VICINI, David Colin, Pichon, Grégoire, Alexandre Louvet, John Gliksberg, Claire Chen, M. Turisini, Andrea Monterubbiano, Jean-Philippe Nominé, Denis Dutoit, Hugo Taboada, Lilia Zaourar, Mohamed Benazouz, Angelos Bilas, Fabien Chaix, Manolis G. H. Katevenis, Nikolaos I. Chrysos, Evangelos Mageiropoulos, Christos Kozanitis, Thomas Moen, Steffen Persvold, Einar Rustad, Sandro Fiore, Fabrizio Granelli, Simone Pezzuto, Raffaello Potestio, Luca Tubiana, Philippe Velha, Flavio Vella, Daniele De Sensi, Salvatore Pontarelli
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Abstract

NET4EXA aims to develop a next-generation high-performance interconnect for HPC and AI systems, addressing the increasing demands of large-scale infrastructures, such as those required for training Large Language Models. Building upon the proven BXI (Bull eXascale Interconnect) European technology used in TOP15 supercomputers, NET4EXA will deliver the new BXI release, BXIv3, a complete hardware and software interconnect solution, including switch and network interface components. The project will integrate a fully functional pilot system at TRL 8, ready for deployment into upcoming exascale and post-exascale systems from 2025 onward. Leveraging prior research from European initiatives like RED-SEA, the previous achievements of consortium partners and over 20 years of expertise from BULL, NET4EXA also lays the groundwork for the future generation of BXI, BXIv4, providing analysis and preliminary design. The project will use a hybrid development and co-design approach, combining commercial switch technology with custom IP and FPGA-based NICs. Performances of NET4EXA BXIv3 interconnect will be evaluated using a broad portfolio of benchmarks, scientific scalable applications, and AI workloads.

Fields of Study & Subjects

Interconnection Networks and Systems Parallel Computing and Optimization Techniques Distributed and Parallel Computing Systems

Publication details

Journal / source
arXiv (Cornell University)
Publication date
27 January 2026
Document type
Preprint