Sophie Robert-Hayek

Orcid: 0000-0003-4359-9124

Affiliations:
  • Atos BDS R&D Data Management, Echirolles, France
  • University of Versailles, Li-PaRAD, Versailles, France
  • Paris-Saclay University, Paris, France (PhD)


According to our database1, Sophie Robert-Hayek authored at least 11 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Application-Agnostic Auto-Tuning of Open MPI Collectives Using Bayesian Optimization.
Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2024

2023
Pattern Matching to improve Accuracy and Efficiency of File Lifecycles Forecasting.
Proceedings of the 14th International Conference on Intelligent Systems: Theories and Applications, 2023

Forecasting File Lifecycles for Intelligent Data Placement in Hierarchical Storage.
Proceedings of the 35th IEEE International Symposium on Computer Architecture and High Performance Computing, 2023

Unraveling the Synoptic puzzle: stylometric insights into Luke's potential use of Matthew.
Proceedings of the Computational Humanities Research Conference 2023, 2023

2022
SHAMan: A Versatile Auto-tuning Framework for Costly and Noisy HPC Systems.
Proceedings of the Optimization and Learning - 5th International Conference, 2022

2021
Auto-tuning of computer systems using black-box optimization : an application to the case of I/O accelerators. (auto-optimisation de systèmes informatiques à l'aide de méthodes d'optimisation de boîte noire : une application au cas des accélérateurs E/S).
PhD thesis, 2021

A comparative study of black-box optimization heuristics for online tuning of high performance computing I/O accelerators.
Concurr. Comput. Pract. Exp., 2021

Record Linkage for Auto-tuning of High Performance Computing Systems.
Proceedings of the Advances in Model and Data Engineering in the Digitalization Era, 2021

2020
SHAMan: an intelligent framework for HPC auto-tuning of I/O accelerators.
Proceedings of the SITA'20: Theories and Applications, 2020

SHAMan: A Flexible Framework for Auto-tuning HPC Systems.
Proceedings of the Modelling, Analysis, and Simulation of Computer and Telecommunication Systems, 2020

2019
Auto-tuning of IO accelerators using black-box optimization.
Proceedings of the 17th International Conference on High Performance Computing & Simulation, 2019


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