While the pre-Exascale is a reality in Europe, the journey towards Exascale entails numerous challenges. With the ever-growing compute nodes and more complex configurations, there are increased component failures, exceeding human-led management system limits, which leads to the obsoletion of the existing toolset and methodologies. A trade-off between performance and energy consumption is no more an option. “Greener”, as important as “faster”, is dictating clients’ mindshare and their purchasing behavior.
E.Eppe, head of solution marketing & portfolio for HPC, AI & Quantum in Atos, will elaborate on how Atos is addressing scalability and efficiency challenges thanks to our proven experience in architecting, manufacturing, and putting in production such systems, at scale.
By applying Machine Learning (ML) or Deep Learning (DL) mechanisms, Atos is enabling our customers to predict component failures, detect and fix automatically IO bottlenecks, identify network congestions, manage power consumptions and related application efficiency.
While decreasing the Atos Super Computer’s Carbon footprint is a clear goal for the future - the Atos DLA (Decarbonization Level Agreement) confirms this engagement - driving efficiently the relation between application efficiency and power usage has the ability to become a true paradigm shift in the Exascale era.
Eventually, Deep Learning will shoehorn into traditional simulation workflows, making hybrid AI-augmented workflows a reality, with remarkable performance improvements in perspective.