Researchers have recently introduced a promising new class of Distributed Constraint Optimization Problem (DCOP) algorithms that is based on sampling. This paradigm is very amenable to parallelization since sampling algorithms require a lot of samples to ensure convergence, and the sampling process can be designed to be executed in parallel. This paper presents GPU-based D-Gibbs (GD-Gibbs), which extends the Distributed Gibbs (D-Gibbs) sampling algorithm and harnesses the power of parallel computation of GPUs to solve DCOPs. Experimental results show that GD-Gibbs is faster than several other benchmark algorithms on a distributed meeting scheduling problem.
