Humboldt-Universität zu Berlin - Mathematisch-Naturwissenschaftliche Fakultät - Modellierung und Analyse komplexer Systeme


Load balancing plays an important role in parallel numerical
simulations. State-of-the-art libraries addressing this problem
base on vertex exchange heuristics that are embedded in a multilevel
scheme. However, these are hard to parallelize due to their
sequential nature. Furthermore, libraries like Metis and Jostle
focus on a small edge-cut and cannot obey constraints like
connectivity and straight partition boundaries, which are important
for some numerical solvers.

In this paper we present an alternative approach to balance the load
in parallel adaptive finite element simulations. We compute a
distribution that is based on solutions of linear equations.
Integrated into a learning framework, we obtain a heuristic that
contains a high degree of parallelism and computes well shaped
connected partitions. Furthermore, our experiments indicate that we
can find solutions that are comparable to those of the two
state-of-the-art libraries Metis and Jostle also regarding the
classic metrics like edge-cut and boundary length.