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.
Titolo: | GD-Gibbs: A GPU-based sampling algorithm for solving distributed constraint optimization problems |
Autori: | |
Data di pubblicazione: | 2014 |
Abstract: | 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. |
Handle: | http://hdl.handle.net/11390/1040254 |
Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |