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[JOB] Postdoc at CEA Saclay 👩‍💻🧑‍💻- Extension of the massively parallel code AMITEX: non-periodic, GPU, coupling


The AMITEX code developed by CEA is a massively parallel code (distributed memory), using Discrete Fourier Transforms, for the numerical simulation of the mechanical behavior of heterogeneous materials. It overcomes the limitations (in size and computation time) encountered by standard finite-element codes used in the same context. A stabilized version of the code, available to the public at, is used by various national (Mines Paris, ONERA, ENSTA Bretagne, I2M-ENSAM Bordeaux, Météo-France, etc.) and international (USA, UK, China, Canada, Germany, Finland, Poland, etc.) teams.

The aim of the post-doc, proposed over two years, is threefold: to extend the field of application of the AMITEX code to non-periodic boundary conditions, to explore a possible adaptation to hybrid CPU/GPU architectures, and to use these extensions to develop FFT-based multi-scale couplings. This objective is built around the 2decomp open source library, on which AMITEX is based, and whose development was taken over in 2022 ([1] by a French-English team. 

The various tasks of the post-doc consist of :

– extend the functionalities of the AMITEX code (non-periodic BC) by introducing different types of discrete transforms (sine, cosine, Fourier) within the 2decomp library,

– explore 2decomp‘s current development towards the use of hybrid CPU/GPU architectures, with the aim of setting up a first AMITEX-GPU code,

– use this new functionality for multi-scale coupling, where a local (refined) simulation interacts with a global (unrefined) simulation [3] via non-periodic boundary conditions,

[1] Rolfo, S.; Flageul, C.; Bartholomew, P.; Spiga, F. & Laizet, S. The 2DECOMP&FFT library: an update with new CPU/GPU capabilities, Journal of Open Source Software, The Open Journal, 2023, 8, 5813

[2] Gélébart L., FFT-based simulations of heterogeneous conducting materials with combined nonuniform Neumann, Periodic and Dirichlet boundary conditions, Eur. Jour. Mech./A solids, Vol. 105, 2024

[3] Noé Brice Nkoumbou Kaptchouang and Lionel Gélébart. Multiscale coupling of fft-based simulations with the ldc approach. Computer Methods in Applied Mechanics and Engineering, 3

Candidate and subject

– The candidate should have completed his/her thesis in the field of numerical mechanics and have a strong and practical interest for computer development, particularly through open source codes (2decomp and AMITEX),

– Although the subject is aimed at (numerical) solid mechanicians, it may also be suitable for (numerical) fluid mechanicians or physicians who wish to open up to solid mechanics,

– Part of the topic concerns extension to GPUs, so prior knowledge of GPU programming will be a plus. However, this point is not essential if the candidate demonstrates solid computer skills that will enable him or her to be trained quickly,

– Depending on the candidate’s professional project and/or initial skills, the part of the work on multi-scale coupling, which will enable the project to be promoted through scientific publications, could be strengthened and/or extended to other types of coupling, and especially multi-physics couplings.


The post-doc, based at CEA Saclay and granted for 2 years, will draw on the skills of L. Gélébart (DES/ISAS/DRMP/SRMA), developer of the AMITEX code, Y. Wang, HPC simulation specialist at the Maison de la Simulation (DRF) and Cédric Flageul (co-developer of the 2decomp library) from the University of Poitiers.


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[JOB] Engineer Position 👩‍💻🧑‍💻- HPC/HPDA support and development


Maison de la Simulation offers 2-3 engineer positions to join the newly created PDI team.

  • Contact: Yushan Wang ( & Benoît Martin (
  • Duration: 3 years
  • Start date: ASAP


Candidates must have at least a Master’s degree or equivalent in Computer Science, Applied Mathematics, or other relevant fields. A PhD degree and working experience in a relevant domain are appreciated. Good programming skills are required.

Applications should be sent to Yushan Wang and Benoît Martin. They should include:

  • a curriculum vitae
  • a motivation letter
  • at least two referees with their email addresses
  • links to Masters/PhD thesis and publications
  • links to software contributions


With the increasing complexity of numerical simulation codes, new approaches are required to analyze the ever-growing amount of data. This requires coupling up-to-date data analysis libraries with the existing highly optimized numerical simulation codes. The PDI Data Interface code coupling library is designed to fulfill this goal.

The open-source PDI Data Interface library is designed and developed for process-local loose coupling in high-performance simulation codes. PDI supports the modularization of codes by inter-mediating data exchange between the main simulation code and independent modules (plugins) based on various libraries. It is developed in modern C++ and offers C, Fortran, and Python application programming interfaces.

PDI offers a reference system similar to Python or C++’s shared_ptr with locking to ensure coherent access by coupled modules. It provides a global namespace (the data store) to share references and implements the Observer pattern to enable modules to react to data availability and modifications. It implements a metadata system that can specify a dynamic type for references based on the value of other data (e.g., array size based on the value of a shared integer). Codes using PDI’s declarative API expose the buffers in which they store data and trigger notifications when significant steps in the simulation are reached. Third-party libraries such as HDF5, SIONlib, or FTI are wrapped in a PDI plugin. A YAML configuration file is used to interleave plugins and additional code without modifying the original application.

Another aspect we explore with PDI is in-situ data analysis, which performs numerical analytics during the simulation. This is necessary due to the ever-growing gap between file system bandwidth and compute capacities. To this end, we are developing the Deisa plugin. This plugin is based on the open-source Dask framework and allows us to transfer data to dedicated processes to perform in-situ analysis.

One of our goals is to establish a feedback mechanism between the in-situ data analysis and the numerical simulation. This allows better resource allocations and on-the-fly simulation monitoring. Another aspect that in-situ analysis enables is using AI methods for HPC and HPDA. For instance, we can have unsupervised detection of rare events during the simulation, which can greatly reduce the amount of produced data, thus reducing stress on the file system.

Work environment

At Maison de la Simulation laboratory, you will join a group of engineers and scientists focusing on all aspects of high-performance computing (HPC). You will have the opportunity to collaborate with PDI users and to introduce new features in the PDI plugin family. As a member of the PDI team, you will also have the opportunity to exchange with the developers of other HPC codes to enrich your skills in HPC code development. To validate your developments, you will be provided with access to the top European supercomputers (Adastra, Jean-Zay, etc.).

Work content

As a member of the newly created PDI team, your primary focus will be developing and maintaining the PDI library.

  • Develop core functionalities and new plugins for PDI
  • Develop the Deisa library
  • User-support
  • Organize training sessions
  • Library packaging and deployment


The successful candidate will master the following skills and knowledge:

  • Proficiency in modern C++ (C++14 and above)
  • Software engineering and library design
  • Modern development environment (Linux, git, CMake, etc.)
  • Communication (writing, presenting, and training)
  • Team-work and integration in an international environment

In addition, the following will be considered a plus:

  • Data analysis libraries such as Dask
  • Knowledge and experience with Python, Fortran and/or GPU computing
  • HPC and parallel libraries such as OpenMP and MPI
  • HPC parallel IO libraries such as HDF5 or NetCDF
  • Experience with supercomputer tools (slurm, sbatch, etc), packaging and deployment

Salary and benefits

The CEA offers salaries based on your degrees and experience. This position offers several advantages:

  • The possibility of joining collaborations with other European laboratories, the United States, and Japan
  • Numerous opportunities to travel internationally (exchanges, conferences, workshops and more)
  • 5 weeks of paid vacation and 4 weeks of RTT per year, and up to 2 days of remote work per week
  • Reimbursement of up to 75% of public transport cards and a free transport network throughout the Ile-de-France region
  • Complimentary health insurance and several company savings plans


seminaire passé seminar

[Seminar] October 24 2023 – Adéquation algorithme architecture pour l’accélération de méthodes d’inversion de données en grande dimension 🧑‍🏫 Nicolas GAC

🧑‍🏫 Nicolas Gac
🌎 Oct 24 2023
☕️ 10:00 AM
🏢 Maison de la Simulation, Batiment Digiteo Saclay, Salle Mandelbrot
🔗 slides

Résumé :

L’amélioration constante de la résolution des instruments parallèlement à la complexité croissante des méthodes de reconstruction basées sur des modèles de plus en plus précis, s’accompagne d’un besoin croissant en puissance de calcul. Les cartes accélératrices composées de GPU ou de FPGA sont une opportunité pour réduire l’écart technologique entre les systèmes d’acquisition et de reconstruction. Dans le contexte particulier de la résolution de problèmes inverses mal posés, mes travaux de recherche en adéquation algorithme-architecture visent à prendre en compte, en amont de la définition des méthodes, le potentiel et les limites des architectures d’accélération. Après une présentation du parallélisme offert par les architectures GPU et FPGA, le contexte algorithmique des méthodes bayésiennes et leur application en reconstruction tomographique seront présentés avec un focus sur l’accélération des opérateurs de projection/rétrojection utilisés en tomographie. Le projet ANR Dark-era portant sur l’accélération des calculs pour la radioastronomie sera ensuite présenté ; ce travail collaboratif vise à construire un outil de prototypage rapide fournissant des simulations exascales des futurs serveurs HPC nécessaires au traitement « temps réel » du flux de données massives du radiotélescope SKA. Enfin, des perspectives sur ces travaux seront exposées.

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[JOB] 👩‍💻🧑‍💻Optimization framework for large deep neural networks

[FR] Plateforme logicielle d’optimisation pour réseaux de neurones profonds de très grande taille

Doctoral Domain : Computer Science

Funding : CIFRE schema, hired in French CDD (36 months) by Huawei France

Work Places : Huawei Paris Research Center (Boulogne-Billancourt) and Maison de la Simulation (University Paris-Saclay)

Industrial Supervision : Dr. Chong Li and Prof. Denis Barthou, Huawei Paris Distributed and Parallel Technologies Lab

Academic Supervision : Prof. Nahid Emad, Université Versailles Saint-Quentin-en-Yvelines

Keywords : Massively Parallelism, Linear Algebra, Performance Optimisation, Deep Learning, Large Models


[JOB] Permanent Researcher Position

System software for in situ HPC/AI coupling

Maison de la Simulation (MdlS) recruits a permanent CEA researcher (ingénieur chercheur permanent) to reinforce its “Science of Computing” (SoC) team.

In order to apply, please send a resume, cover letter, references and support letters to You can use the same email address for requests of information about the position. Applications will be evaluated from May the 15th 2023 and until the position is filled.


Maison de la Simulation is a joint research and engineering laboratory of CEA, CNRS, Université Paris-Saclay and Université de Versailles Saint-Quentin-en-Yvelines localized on the plateau de Saclay campus next to Paris. It specializes in high-performance computing. The “Science of Computing” team conducts research, builds expertise and engineers tools in domains underlying HPC: computer science and applied mathematics.

The team co-leads the 5-years French ExaDoST project, part of NumPEx, that will design the software stack for data handling on the upcoming French and potentially European Exascale super-computers.


During the first five years, the selected candidate will contribute to the ExaDoST project. They will conduct research and design tools for data handling at Exascale, working both on the user facing API and on system software for in situ HPC/AI coupling. They will work in close collaboration with the other members of the project to design a modular software stack that should be used on the upcoming French Exascale supercomputer as well as more globally on French, European, and worldwide supercomputers.

To achieve these goals, the candidate will take part in the recruitment and management of the group of temporary engineer and young researchers dedicated to this project. They will leverage the expertise and tools already developed in the team, including the PDI and Deisa libraries.

As time evolves, the selected candidate will develop their own research and projects in the domains of the team. At the conclusion of ExaDost, they will have a large latitude to direct their research toward the directions they feel would serve the team and laboratory best.

Main activities

The candidate will:

  • conduct their research in the domain of runtime and system software for in situ analytics and HPC/AI coupling,
  • take part in the design and implementation of libraries and tools that make the results of this research available to users of HPC in production,
  • take part in the NumPEx project, and related activities of management and reporting,
  • participate to the management of temporary engineers and young researchers.

Skills and competencies

The successful candidate will hold a PhD thesis in computer science or in another field with a strong experience in computer science. They should master the following skills and competencies:

  • good knowledge of HPC parallel architectures, operating systems, and application programming,
  • knowledge of the design of existing system tools and libraries for HPC data handling (IO, in situ processing, coupling, checkpoint writing, etc.),
  • good programming skills in C++, and associated developments tools (Cmake, git, etc.),
  • good programming skills in python, and associated ecosystem,
  • autonomy, interest for team-work in an international context, leadership.

Salary and advantages

CEA “ingénieur-chercheur” positions offer a very competitive salary in French research ecosystem, indexed on diplomas and experience. In addition, the selected candidate will benefit from many advantages:

  • possibility to leverage existing collaborations of MdlS in Europe, US and Japan as well as international conferences with support for travel,
  • up to 3 days of remote work per week,
  • reimbursement of public transport costs (75%) and subsidized catering service,
  • included pension plan and health coverage (French social security plus CEA-specific insurance),
  • 9 full weeks of holidays per year.



Pascal TREMBLIN (CEA senior research scientist)

Executive assistant

Valérie BELLE

Permanent members

Science by Computing Team
  • Team Leader:
    Karim HASNAOUI (CNRS research engineer)
  • Edouard AUDIT (CEA senior research scientist)
  • Daniel BORGIS (CNRS senior research scientist)
  • Mathieu LOBET (CEA research scientist)
  • Riccardo SPEZIA (CNRS senior research scientist)
  • Yushan WANG (CEA research scientist)
  • Charles PROUVEUR (CNRS research engineer)
Science of Computing Team
  • Team Leader:
    Julien BIGOT (CEA research scientist)
  • Victor ALESSANDRINI (CNRS research scientist emeritus)
  • Yuuichi ASAHI (CEA research scientist)
  • Benoît MARTIN (CEA research scientist)
  • Simplice DONFACK (CNRS research engineer)
  • Thomas DUFAUD (UVSQ associate professor)
  • Nahid EMAD (UVSQ full professor)
  • Martial MANCIP (CNRS research engineer)
  • Thomas PADIOLEAU (CEA research scientist)
Project Officer
  • Julien THELOT

Fixed-term members

  • Simon EL-OUAFA
  • Juan Jose SILVA CUEVAS
  • Salem KHELLAL
  • Hiba TAHER
  • Paul ZEHNER
  • Thierry ANTOUN
PhD students
  • Jean-Marc LUDE
  • Aymeric MILLAN
  • François-Xavier MORDANT
  • Sara MOUKIR
  • Quentin PETIT
  • Kevin PEYEN
  • Mathilde POVEDA
  • Pascal WANG
  • Zineb ZIANI
  • Merieme BOURENANE
  • Benjamin GERBE
  • Melissa LARBI
  • Florent NYBERG
  • Pierre-Antoine RACLIUS
  • Sarah SAADOUNI

Former members