Data locality and workload balance are key factors for getting high
  performance out of data-parallel programs on multiprocessor
  architectures. Data-parallel languages such as High Performance
  Fortran (HPF) offer thus means allowing a programmer both to specify
  data distributions as well as to change them dynamically in order to
  maintain these properties. On the other hand, redistributions can be
  quite expensive, and significantly degrade a program's performance.
  They must thus be reduced to a minimum. In this article we
  present a novel, aggressive approach for avoiding unnecessary
  remappings which works by eliminating partially dead and
  partially redundant distribution changes.  Basically, this
  approach evolves from extending and combining two algorithms for
  these optimizations achieving each on its own optimal results. In
  distinction to the sequential setting, the data-parallel setting
  leads naturally to a family of algorithms of varying power and
  efficiency allowing requirement-customized solutions.  The power and
  flexibility of the new approach are demonstrated by various
  examples, which range \from typical HPF fragments to real world
  programs.  Performance measurements underline its importance and
  show its effectivity on different hardware platforms and different
  settings.