Vorträge

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

Parareal is the most studied Parallel-in-Time method; by introducing parallelism in the time dimension, it allows to relieve communication bottlenecks that appear when parallelism is used only in the spatial dimension.
An expensive part of Parareal is the sequential solve using the coarse operator. So, for performance reasons, it can be interesting to consider the sequential operator not only on a coarser grid in time but also in space.
In this talk, we will discuss an alternative approach to the Generating Function Method (GFM) for computing Parareal bounds and how it can be used to compute linear and superlinear bounds.
We will then extend the analysis to Parareal with spatial-coarsening (coarsening factor 2 in space and time) and discuss the associated challenges. Finally, numerical results for the heat equation will be provided.

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