  We have developed a technique to accelerate the acquisition of effectively
uncorrelated configurations for off-lattice models of dense polymer melts which
makes use of both parallel tempering and large scale Monte Carlo moves. The
method is based upon simulating a set of systems in parallel, each of which has
a slightly different repulsive core potential, such that a thermodynamic path
from full excluded volume to an ideal gas of random walks is generated. While
each system is run with standard stochastic dynamics, resulting in an NVT
ensemble, we implement the parallel tempering through stochastic swaps between
the configurations of adjacent potentials, and the large scale Monte Carlo
moves through attempted pivot and translation moves which reach a realistic
acceptance probability as the limit of the ideal gas of random walks is
approached. Compared to pure stochastic dynamics, this results in an increased
efficiency even for a system of chains as short as $N = 60$ monomers, however
at this chain length the large scale Monte Carlo moves were ineffective. For
even longer chains the speedup becomes substantial, as observed from
preliminary data for $N = 200$.
