output
20172026
most citedPrimal-Dual Algorithms with Predictions for Online Bounded Allocation and Ad-Auctions Problems

2 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions

15 papers

cs.DC2026

Nix to the Rescue for a Reproducible HPC-AI Software Stack

Wenke Du, Jean-Marc Gratien, Raphael Gayno +1

Reproducibility in HPC remains difficult under the constraints of production supercomputers: no root access, limited internet, and software stacks that increasingly span C/C++, For…

cs.LG2026

Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training

Pierre Cesar, Sofya Dymchenko, Abhishek Purandare +1

Data-driven PDE surrogates are trained with data produced by numerical PDE solvers. However, when the surrogate's goal is to generalize across a wide range of PDE configurations (e…

cs.DC2025

Analysis of the carbon footprint of HPC

Abdessalam Benhari, Yves Denneulin, Frédéric Desprez +2

The demand in computing power has never stopped growing over the years. Today, the performance of the most powerful systems exceeds the exascale. Unfortunately, this growth also co…

cs.DC2025

Bridding OT and PaaS in Edge-to-Cloud Continuum

Carlos J Barrios, Yves Denneulin

The Operational Technology Platform as a Service (OTPaaS) initiative provides a structured framework for the efficient management and storage of data. It ensures excellent response…

cs.DC2024★ 1 cited

Performance Debugging through Microarchitectural Sensitivity and Causality Analysis

Alban Dutilleul, Hugo Pompougnac, Nicolas Derumigny +4

Modern Out-of-Order (OoO) CPUs are complex systems with many components interleaved in non-trivial ways. Pinpointing performance bottlenecks and understanding the underlying causes…

cs.CY2024

Green HPC: An analysis of the domain based on Top500

Abdessalam Benhari, Denis Trystram, Fanny Dufossé +2

The demand in computing power has never stopped growing over the years. Today, the performance of the most powerful systems exceeds the exascale and the number of petascale systems…

cs.DS2024★ 2 cited

Primal-Dual Algorithms with Predictions for Online Bounded Allocation and Ad-Auctions Problems

Eniko Kevi, Nguyen Kim Thang

Matching problems have been widely studied in the research community, especially Ad-Auctions with many applications ranging from network design to advertising. Following the variou…

cs.AI2023

Training Deep Surrogate Models with Large Scale Online Learning

Lucas Meyer, Marc Schouler, Robert Alexander Caulk +2

The spatiotemporal resolution of Partial Differential Equations (PDEs) plays important roles in the mathematical description of the world's physical phenomena. In general, scientis…

cs.DC2023

A Framework for Large Scale Particle Filters Validated with Data Assimilation for Weather Simulation

Sebastian Friedemann, Kai Keller, Yen-Sen Lu +2

Particle filters are a group of algorithms to solve inverse problems through statistical Bayesian methods when the model does not comply with the linear and Gaussian hypothesis. Pa…

cs.AI2022

Simulation-Based Parallel Training

Lucas Meyer, Alejandro Ribés, Bruno Raffin

Numerical simulations are ubiquitous in science and engineering. Machine learning for science investigates how artificial neural architectures can learn from these simulations to s…

cs.DC2022

Understanding the Energy Consumption of HPC Scale Artificial Intelligence

Danilo Carastan dos Santos

This paper contributes towards better understanding the energy consumption trade-offs of HPC scale Artificial Intelligence (AI), and more specifically Deep Learning (DL) algorithms…

cs.LG2021

Deep Surrogate for Direct Time Fluid Dynamics

Lucas Meyer, Louen Pottier, Alejandro Ribes +1

The ubiquity of fluids in the physical world explains the need to accurately simulate their dynamics for many scientific and engineering applications. Traditionally, well establish…

cs.CE2020

An elastic framework for ensemble-based large-scale data assimilation

Sebastian Friedemann, Bruno Raffin

Prediction of chaotic systems relies on a floating fusion of sensor data (observations) with a numerical model to decide on a good system trajectory and to compensate nonlinear fee…

math.ST2019★ 1 cited

Large scale in transit computation of quantiles for ensemble runs

Alejandro Ribes, Théophile Terraz, Bertrand Iooss +2

The classical approach for quantiles computation requires availability of the full sample before ranking it. In uncertainty quantification of numerical simulation models, this appr…

cs.GR2017★ 2 cited

QuickCSG: Fast Arbitrary Boolean Combinations of N Solids

Matthijs Douze, Jean-Sébastien Franco, Bruno Raffin

QuickCSG computes the result for general N-polyhedron boolean expressions without an intermediate tree of solids. We propose a vertex-centric view of the problem, which simplifies…