  We implemented a parallel version of the multicanonical algorithm and applied
it to a variety of systems with phase transitions of first and second order.
The parallelization relies on independent equilibrium simulations that only
communicate when the multicanonical weight function is updated. That way, the
Markov chains efficiently sample the temporary distributions allowing for good
estimations of consecutive weight functions.
  The systems investigated range from the well known Ising and Potts spin
systems to bead-spring polymers. We estimate the speedup with increasing number
of parallel processes. Overall, the parallelization is shown to scale quite
well. In the case of multicanonical simulations of the $q$-state Potts model
($q\ge6$) and multimagnetic simulations of the Ising model, the optimal
performance is limited due to emerging barriers.
