  In the paper, a parallel Tabu Search algorithm for the Resource Constrained
Project Scheduling Problem is proposed. To deal with this NP-hard combinatorial
problem many optimizations have been performed. For example, a resource
evaluation algorithm is selected by a heuristic and an effective Tabu List was
designed. In addition to that, a capacity-indexed resource evaluation algorithm
was proposed and the GPU (Graphics Processing Unit) version uses a homogeneous
model to reduce the required communication bandwidth. According to the
experiments, the GPU version outperforms the optimized parallel CPU version
with respect to the computational time and the quality of solutions. In
comparison with other existing heuristics, the proposed solution often gives
better quality solutions.
