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


Load balancing is an important issue in parallel numerical
simulations. However, state-of-the-art libraries addressing this
problem show several deficiencies: they are hard to parallelize,
focus on small edge-cuts rather than few boundary vertices, and
often produce disconnected partitions.

We present a distributed implementation of a load balancing
heuristic for parallel adaptive FEM simulations. It is based on a
disturbed diffusion scheme embedded in a learning framework. This
approach incorporates a high degree of parallelism that can be
exploited and it computes well-shaped partitions as shown in
previous publications. Our focus lies on improving the condition of
the involved matrix and solving the resulting linear systems with
local accuracy. This helps to omit unnecessary computations as well
as allows to replace the domain decomposition by an alternative data
distribution scheme reducing the communication overhead, as shown by
experiments with our new MPI based implementation.

Keywords: Load balancing, graph partitioning, parallel adaptive FEM