Guillaume AUPY
Author's posts
Nov 01
Julien Herrmann joins the team as Postdoc
Julien Herrmann has obtained his PhD from ENS Lyon in 2015. He will be working with Guillaume Aupy and Olivier Beaumont (RealOpt) on the Influence of local storage capacities on task based schedulers, with a focus on specific graph structure such as those involved in backpropagation. He is funded by ANR Dash (ANR-17-CE25-0004) and a …
Jun 28
Talk by Navjot Kukreja (Imperial College) on June 28th, 2018
High-level abstractions for checkpointing in PDE-constrained optimisation Gradient-based methods for PDE-constrained optimization problems often rely on solving a pair of forward and adjoint equations to calculate the gradient. This requires storing large amounts of intermediate data, limiting the largest problem that might be solved with a given amount of memory. Checkpointing is an approach that …
Jun 28
Talk by Jan Hückelheim (Imperial College) on June 28th, 2018
Algorithmic differentiation in high-performance computing: challenges and opportunities in optimisation,uncertainty quantification, and machine learning Gradients are useful in countless applications, e.g. gradient-based shape optimisation in structural dynamics, adjoint methods in weather forecasting, or the training of neural networks. Algorithmic differentiation (AD) is a technique to efficiently compute gradients of computer programs, and has undergone decades …
Dec 04
Valentin Honoré joins TADaaM as a PhD student
Valentin will work in Partitioning strategies for High-Throughput Applications. In particular his focus will be on Hierarchical memories, used in the context of in-situ/in-transit frameworks. His thesis is supervised by Guillaume Aupy and Brice Goglin. Welcome Valentin :).
Dec 04
Guillaume Aupy obtained an ANR JCJC — DASH
Guillaume Aupy was granted an ANR JCJC from the AAPG 2017 call. the ANR is scheduled to start on March 1st, more information can be found on the dedicated website. The goal of the project is to study I/O congestion in supercomputers and to provide new static and dynamic algorithms to minimize it.
