Explores the challenges of the energy transition and exascale computing in High Performance Computing, focusing on renewable energy scientific challenges and the development of exascale-enabled solvers.
Explores high-performance OPF solvers, addressing challenges in power system optimization and showcasing significant speed-ups and memory-efficient approaches.
Covers the Conjugate Gradient method for solving linear systems without pre-conditioning, exploring parallel computing implementations and performance predictions.
Covers the adaptation of analytics systems to modern hardware and data challenges, focusing on efficiency and scalability through innovative approaches and hybrid systems.
Outlines the Master in Computational Science and Engineering program at EPFL, detailing its structure, projects, and career opportunities for graduates.
Outlines the Master in Computational Science and Engineering at EPFL, detailing its structure, admission criteria, and career opportunities for graduates.
