  In recent years, there has been renewed interest in closing the performance
gap between state-of-the-art planning solvers and generalized planning (GP), a
research area of AI that studies the automated synthesis of algorithmic-like
solutions capable of solving multiple classical planning instances. One of the
current advancements has been the introduction of Best-First Generalized
Planning (BFGP), a GP algorithm based on a novel solution space that can be
explored with heuristic search, one of the foundations of modern planners. This
paper evaluates the application of parallel search techniques to BFGP, another
critical component in closing the performance gap. We first discuss why BFGP is
well suited for parallelization and some of its differentiating characteristics
from classical planners. Then, we propose two simple shared-memory parallel
strategies with good scaling with the number of cores.
