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Non classé

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

Subject

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 https://amitexfftp.github.io/AMITEX/, 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 (https://github.com/2decomp-fft/2decomp-fft)[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.

Details

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.

Contacts 

lionel.gelebart@cea.fr

yushan.wang@cea.fr

cedric.flageul@univ-poitiers.fr

Categories
Non classé

[JOB] M2 coupling IA for HPC Internship

Enhancing heat equation Simulations with AI-Driven In-Situ Analysis Using High-Performance Computing

Superviseur : Martial MANCIP, Benoît MARTIN, Yushan WANG
Durée du stage : 6 months (from february 2024)
Langue : french or english
Lieu : Maison de la Simulation, CEA Saclay

Context

This master internship focuses on leveraging Artificial Intelligence (AI) for High-Performance Computing (HPC) simulations in the field of heat equation.
The project aims to integrate AI-based techniques within a heat simulation code to enable in-situ analysis and inference, optimizing the post-treatment process and enhancing its outputs capabilities.

Objective

The primary goal of this internship is to develop and implement an AI-driven methodology within a heat equation simulation framework for real-time heat source detection (so-call event) and labeling in small simulation boxes. These labeled events, represented through 3D renderings or ensembles of 2D slices, will serve as training, validation, and test datasets for the AI model. Subsequently, the trained AI model will be integrated into the DEISA framework available with the simulator to conduct in-situ simulations with enhanced inference capabilities.

DEISA: dask-enabled in situ analytics
https://cea.hal.science/hal-03509198v1

Methodology

Data Generation and Labeling: Use the HPC simulator to create small simulation boxes. Implement algorithms to detect and label one or two specific events within these boxes.
Dataset Preparation: Construct training, validation, and test sets using 3D renderings or 2D slices generated from the labeled events.
AI Model Development: Design and train an AI model, such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), to recognize and classify events within the simulation data.
Integration with simulator and DEISA:
Implement the AI model within the DEISA framework for in-situ simulations anylisis.
Use the AI model to perform real-time inference during simulation runs.

Expected Outcomes

A labeled dataset of events within small simulation boxes.
Trained AI model capable of accurately detecting and labeling events in simulation data.
Integration of the AI model within the DEISA framework for in-situ simulations with enhanced inference capabilities to be abble to feedback high frequency outputs in the simulation.
Evaluation of the AI’s performance in improving simulation accuracy, efficiency, and predictive capabilities.

We expect to add the use of a feedback process from DEISA to the simulation to switch on high frequency outputs when the AI detects one event and stops them with it has vanished.

Conclusion

The successful implementation of AI techniques within the simulator code for in-situ analysis has the potential to significantly enhance the efficiency of the output production of simulations.

This internship provides a stepping stone towards the integration of cutting-edge AI methodologies with HPC simulations, opening doors for more precise predictions and deeper insights into complex phenomena.

Candidate profil

  • Parallel computing
  • C++
  • Python3
  • AI : DL with Tensorflow or Pytorch

How to candidate

Send cover letter and CV to contact@mdls.fr

Categories
Non classé seminaire passé seminar

[SEMINAR] January 2023 – Simulations numériques ab initio de l’irradiation ionisante de la matière 🧑‍🏫 Auréllien de la Lande

🧑‍🏫 Aurélien de la Lande, CNRS
🌎 January 25 2023

Nous avons développé à l’Institut de Chimie Physique d’Orsay des approches de simulation ab initio originales pour simuler le dépôt d’énergie par des ions rapides ou des photons XUV dans des systèmes moléculaires de grandes tailles, tels que ceux rencontrés en biologie1. Durant ce séminaire, nous intro- duirons les équations du mouvement des électrons dans le cadre de la théorie de la fonctionnelle de la densité2. 

Nos codes de simulation reposent sur de nouveaux algorithmes de la théorie de la fonctionnelle de la densité dépendant du temps permettant de simuler des systèmes de taille nanométrique et inhomo- gènes3,4. L’une des astuces principales est de recourir à des densités électroniques auxiliaires permettant de calculer la répulsion coulombienne et les effets liés à la nature quantique des électrons (échange et corrélation). Le couplage avec la librairie ScaLapack permet une réduction importante du cout de calcul du propagateur3. Pour aller plus loin une interface avec la libraire Magma a récemment été réalisée. La réduction du coût de calcul est remarquable et permet d’entrevoir des applications sans précédent en terme de taille de systèmes simulés. 

J’illustrerai l’apport de ces approches par diverses études récentes du groupe. Un premier exemple a trait à l’irradiation comparée d’oligomères d’ADN solvatés par des protons, de noyaux d’hélium ou de carbone (Fig. 1)5. L’étude a permis de mettre en évidence des processus clés de l’étape physique de l’irradiation ; par exemple le mécanisme d’ionisation par flux-et-reflux du nuage électronique, la localisation des électrons secondaires ou encore les probabilités d’ionisation des bases d’ADN ou du solvant5. Dans un second exemple je mettrai en évidence un effet de taille remarquable lors le processus d’ionisation d’acide aminés, de peptides et de protéines par des photons ionisant XUV. Cette découverte permet de faire des hypothèses sur les sites d’ionisation primaires possibles du milieu cellulaire par ce type de rayonnement. 

Categories
Non classé seminaire passé

[SEMINAR] February 2019 – Hercules 🧑‍🏫 Olivier Bressand

🧑‍🏫 Olivier Bressand, CEA-DAM, France
🌎 February 2019

Abstract

Hercules is a CEA-DAM platform for managing data produced by simulation codes. It integrates different I/O services to read, write in parallel database in the framework of protection / recovery, intercode (coupling or code sequence) and post-processing (visualization and analysis). It is based on a data model that covers many domains of simulation (structured, unstructured, AMR-block, AMR-tree based, multi-fluid, laser, atom, Euler, Lagrange, ale, 1D, 2D, 3D) and provides services to produce, filter, and disaggregate data in the sequential or parallel HPC application.

Categories
Non classé seminaire passé

[SEMINAR] February 2019 – MPC, The Multi-Processor Computing Framework 🧑‍🏫 Julien Jaeger

🧑‍🏫 Julien Jaeger, CEA-DAM, France
🌎 February 2019

Abstract

The MPC (Multi-Processor Computing) framework provides a unified parallel runtime designed to improve the scalability and performances of applications running on clusters of (very) large multiprocessor/multicore NUMA nodes. Thanks to its design, MPC allows mixed-mode programming models and efficient interaction with the HPC software stack. MPC provides implementations for the MPI, OpenMP and POSIX Threads standards. All these standards can be mixed together in an efficient way, thanks to process virtualization, and the sharing of information and resources

Categories
Non classé seminaire passé

[SEMINAR] December 2012 – Software optimization for petaflops/s scale Quantum Monte Carlo simulations 🧑‍🏫 by Anthony SCEMAMA

🧑‍🏫 Anthony Scemama, Research Engineer at the Laboratoire de Chimie et de Physique Quantiques of IRSAMC
🌎 December 2012

Quantum chemistry is known to be one of the grand challenges of modern science since many fundamental and applied fields are concerned (drug design, micro-electronics, nanosciences,…). To investigate all these fascinating problems is a tremendous task since highly accurate solutions of the fundamental underlying Schrödinger equation for a (very) large number of electrons need to be determined. The use of Quantum Monte Carlo methods is an emerging alternative approach to usual methods since they can take advantage of massively parallel architectures. In this talk the QMC=Chem program we develop in Toulouse will be presented, as well as the different strategies we used to reach the petaflops/s scale.

Slides :