Result of the NumPEx call for action
The NumPEx program is launching its first call for projects to support advances in high-performance computing (HPC), high-performance data analysis (HPDA) and artificial intelligence (AI). Our France 2030 research program aims to develop software capable of operating future exascale machines, and to prepare the main scientific and industrial application codes.
This call is structured around three axes:
- Emerging AI methods, algorithms and software for scientific computing and HPC for AI.
- Programming models adapted to accelerated architectures.
- Workflows for scientific data analysis, with the SKA project as a use case.
DAIMOS - Distributed AI Model training Optimization at Scale
Project leader: Julien Herrmann, CNRS researcher
Training large-scale AI models presents major challenges, particularly regarding computational
cost and energy efficiency. This project addresses these issues by developing a new software stack
for large-scale deep learning, based on a close integration of algorithmic advances, systems-level
optimization, and concrete application use cases. It directly supports the priorities of the NumPEx
PEPR program on HPC for AI.
SAGe-HPC - Smart strateGies for multi-fidelity optimization in Exascale HPC Environments
Project leader: Laëtitia Giraldi, Inria researcher
The SAGE-HPC project aims to develop a scalable, open, and interoperable software platform for multifidelity
optimization of complex physical problems in exascale high-performance computing (HPC) environments.
Solving such optimization problems poses a major scientific challenge due to the complexity
of the physical phenomena involved and the computational cost associated with high-fidelity simulations.
To overcome this challenge, the project leverages both the coordinated use of variable-fidelity models —
where simplified, low-cost models guide the exploration of the solution space, and high-fidelity models are
used selectively to refine the results — and the massive exploitation of exascale HPC resources, enabling
large-scale parallel processing of these approaches.
KOKTAILS - Kokkos by translation and interoperability leveraged in software
Project leader: Stéphane de Chaisemartin, IFPEN engineer
The KOKTAILS project aims to enhance the portability of simulation software on Exascale computing
architectures, by contributing to the development of a sovereign software stack adapted to
GPU-based supercomputers. It is part of the NumPEx PEPR strategy and contributes to French
digital sovereignty in high-performance computing (HPC). It includes the development of scalable
middleware to guarantee performance portability on various GPU architectures, including European
processors such as SiPearl Rhea. The project thus contributes to the transition of existing applications
to Exascale computing, through the creation of an open-source ecosystem in line with
European sovereignty policy.
ASTRA - Advanced FR-SRC Tasks and Resource Allocation
Project leader: Marc-Antoine Miville-Deschênes, CNRS researcher
This research project addresses the critical transformation underway in radio astronomy, driven by next-generation observatories such as LOFAR2.0 and the SKA. These instruments are producing massive, heterogeneous datasets distributed across multiple sites, which cannot be efficiently handled using legacy data processing approaches. The project aims to overcome these structural bottlenecks by developing a unified, scalable digital platform that federates HPC, cloud, and object storage resources. It will support the execution of complex workflows (including AI-based processing) across heterogeneous infrastructures through modern containerization technologies. Key principles such as data provenance, reproducibility, and energy-aware computing will be integrated to support both interactive and automated scientific workflows.
