Tools and Optimization for high Performance Applications and Learning

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).