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
  computations.