@inproceedings{595e2073f1ef46bda53a36c683f8e97b,
title = "GD-Gibbs: A GPU-based sampling algorithm for solving distributed constraint optimization problems",
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.",
keywords = "DCOP, GPU, Gibbs, Sampling",
author = "Ferdinando Fioretto and Federico Campeotto and {Da Rin Fioretto}, Luca and William Yeoh and Enrico Pontelli",
note = "Publisher Copyright: Copyright {\textcopyright} 2014, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.; 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014 ; Conference date: 05-05-2014 Through 09-05-2014",
year = "2014",
language = "English (US)",
series = "13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014",
publisher = "International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)",
pages = "1339--1340",
booktitle = "13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014",
}