We introduce a practical partitioning technique designed for parallelizing Position Based Dynamics, and exploiting
the ubiquitous multi-core processors present in current commodity GPUs. The input is a set of particles whose
dynamics is influenced by spatial constraints. In the initialization phase, we build a graph in which each node
corresponds to a constraint and two constraints are connected by an edge if they influence at least one common
particle. We introduce a novel greedy algorithm for inserting additional constraints (phantoms) in the graph
such that the resulting topology is q-colourable, where ˆ qˆ ≥ 2 is an arbitrary number. We color the graph, and
the constraints with the same color are assigned to the same partition. Then, the set of constraints belonging to
each partition is solved in parallel during the animation phase. We demonstrate this by using our partitioning
technique; the performance hit caused by the GPU kernel calls is significantly decreased, leaving unaffected the
visual quality, robustness and speed of serial position based dynamics