SKM 2023 – wissenschaftliches Programm
DY 57.1: Vortrag
Freitag, 31. März 2023, 09:30–09:45, ZEU 250
Efficient integration of short-range models on complex networks — •Jeffrey Kelling1,2, Géza Ódor3, Lilla Barancsuk3, Shengfeng Deng3, Bálint Hartmann3, and Sibylle Gemming2 — 1Chemnitz University of Technology, Chemnitz, Germany — 2Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany — 3Centre for Energy Research, Budapest, Hunguary
Complex, hierarchical or random network topolgies can give rise to unique behavior in many physical models. We study dynamical synchronization behavior in Kuramoto models on power grids and brain connectomes with millons of connections and O(100k) nodes. At these scales it is crucial to use the sparsity when computing derivatives, which, due to the random network structure, makes employing modern parallel hardware tricky. Here, we present our approach to numerically solving large systems ordinary differential equations on random directed graphs, where we focus on the computationally expensive task of computing derivatives and leave the common integration step to the boost::odeint library. Our application can utilize both parallel CPUs and GPUs. We also provide an overview of our results on human and fly brain connectomes as well as failure cascades in power grids and provide a measure of the advantage gained from our computational optimization efforts.