  The most successful parallel SAT and MaxSAT solvers follow a portfolio
approach, where each thread applies a different algorithm (or the same
algorithm configured differently) to solve a given problem instance. The main
goal of building a portfolio is to diversify the search process being carried
out by each thread. As soon as one thread finishes, the instance can be deemed
solved. In this paper we present a new open source distributed solver for
MaxSAT solving that addresses two issues commonly found in multicore parallel
solvers, namely memory contention and scalability. Preliminary results show
that our non-portfolio distributed MaxSAT solver outperforms its sequential
version and is able to solve more instances as the number of processes
increases.
