Talks

The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and mitigation and to guide rapid responses to extreme events. Forecasting models are limited by computational costs and, therefore, often generate coarse-resolution predictions. Two common ways to decrease computational efforts with DL are downscaling, the increase of the resolution directly on the predicted climate variables, and emulation, the replacement of model parts to achieve faster runs initially. Here, we look at several downscaling tasks and an aerosol emulation problem. While deep learning shows promising results it may not obey simple physical constraints, such as mass conservation or mass positivity. We tackle this by investigating both soft and hard constraining methodologies in different setups, showing that incorporating hard constraints can be beneficial for both downscaling and emulation problems.

Zoomlink:
https://tuhh.zoom.us/j/81920578609?pwd=TjBmYldRdXVDT1VkamZmc1BOajREZz09

The theory of evolutionary equations, afforded by Rainer Picard (Dresden) et al., provides a well-posedness theorem applicable to a vast amount of linear PDEs including heat, wave and Maxwell's equations as well as equations including fractional derivatives and integrals. In this talk, I will discuss this well-posedness theorem in the autonomous case. I will show how to impose initial and boundary conditions on such evolutionary equations, and I will present a possible evolutionary approach to control theory for PDEs.

Graphics Processing Units (GPUs), initially designed as accelerators for graphics applications, have revolutionized the computing landscape with their unparalleled computational prowess. Today, GPU-accelerated systems are present everywhere – for example, in our smartphones, cars, and supercomputers. GPU-accelerated systems are transforming the world in many ways, and several exciting possibilities, such as digital twins and precision medicine are on the horizon. While GPU-accelerated systems are desirable, their optimal utilization is crucial; otherwise, they can be very expensive in terms of power and energy consumption, which is not good as we aspire to reduce our carbon footprint. A single GPU can draw up to 700 watts, while GPU-powered supercomputers scale to the energy-hungry range of 1 to 10 megawatts.
In this presentation, I will talk about the performance, power, and energy efficiency of GPUs. I will present a GPU power simulator that we developed to estimate the power and energy efficiency of GPUs and show how we can use the simulator to investigate bottlenecks that cause low performance and low energy efficiency, highlighting the wide gap between the achieved energy efficiency of GPUs and the energy-efficiency aim of exascale computing.
Finally, I will briefly highlight two ongoing projects aimed at harnessing GPUs effectively within High-Performance Computing (HPC) clusters.
In the first project, we are developing techniques to predict the scalability of applications on HPC clusters. The project aims to automatically choose the best number of nodes for an application depending on its scalability. In the second project, we are developing a tool to enable automatic optimization of HPC applications on NVIDIA Hopper (and the next generation) GPUs. As we navigate the intricate interplay of performance, power, and energy efficiency, we embark on a quest to maximize the transformative potential of GPUs while minimizing their environmental footprint.

Zoomlink:
https://tuhh.zoom.us/j/81920578609?pwd=TjBmYldRdXVDT1VkamZmc1BOajREZz09