We propose to structure our research around two main application fields: linear multi-dimensional algebra and solvers on the one hand, and training in particular of deep learning networks on the other hand. In these two domains, our contributions will be organized around three main research axes: the use of task based runtime systems (to provide robust solutions and to increase the portability in the context of heterogeneous large scale platforms), the use of compression (to limit memory footprint and data transfers) and the minimization of energy consumption and carbon impact (using an approach of rewriting algorithms and placement strategies to limit data movements).
In the Student Cluster Competition of the SuperComputing'23 conference, the TOPAL team participated in the organization of the Reproducibility Challenge, based on a paper published at SC22 on the Symmetric Block Cyclic distribution.
Alycia Lisito will be a PhD student in the team, starting from January 1st, 2024. She will work on a flexible and portable implementation of the HPL benchmark, using Chameleon and StarPU, in collaboration with Eviden.
The team is part of the organizing commitee of WANT, the Workshop on Advancing Neural Network Training which will be part of NeurIPS 2024. This workshop will address Computational Efficiency, Scalability, and Resource Optimization.
Topal team kickoff The Inria center of University of Bordeaux will celebrate the kickoff of the TOPAL project team, on June 12th, 2023, at 4:30 PM. The event will be followed by a cocktail.
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