Dynamic programming on tree decompositions is often a key for solving a widerange of optimization problems that would otherwise be intractable. Operations on tree decompositions are amenable to parallelization. However, a systematic approach towards such parallelization is still lacking. In this work, we present a generic and flexible framework for parallel dynamic programming on tree decompositions that can be easily adapted to solve any graph theoretic optimization problem on graphs of bounded treewidth. We show the effectiveness of our framework using the Dominating Set problem as a case study. Our experiments show notable speedups compared to the serial approach.