This paper explores the joint use of decomposition methods and parallel computing for solving constraint satisfaction problems and introduces a framework called Parallel Decomposition for Constraint Satisfaction Problems (PD-CSP). The main idea is that the set of constraints are first clustered using a decomposition algorithm in which highly correlated constraints are grouped together. Next, parallel search of variables is performed on the produced clusters in a way that is friendly for parallel computing. In particular, for the first step, we propose the adaptation of two well-known clustering algorithms (k-means and DBSCAN). For the second step, we develop a GPU-based approach to efficiently explore the clusters. The results from the extensive experimental evaluation show that the PD-CSP provides competitive results in terms of accuracy and runtime.

中文翻译：

---

当分解遇到约束满足问题


本文探讨了联合使用分解方法和并行计算来解决约束满足问题，并介绍了一种称为约束满足问题并行分解（PD-CSP）的框架。主要思想是首先使用分解算法对约束集进行聚类，其中高度相关的约束被分组在一起。接下来，以有利于并行计算的方式对生成的集群执行变量的并行搜索。特别是，对于第一步，我们建议采用两种著名的聚类算法（k-means 和 DBSCAN）。第二步，我们开发了一种基于 GPU 的方法来有效地探索集群。广泛的实验评估结果表明，PD-CSP 在准确性和运行时间方面提供了具有竞争力的结果。