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When the Decomposition Meets the Constraint Satisfaction Problem
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DOI：10.1109/ACCESS.2020.3038228.png)
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摘要
En 中文
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.
Keyword:
Graphics processing units
Clustering algorithms
Complexity theory
Load management
Data mining
Search problems
Partitioning algorithms
CSP
decomposition
scalability
GPU
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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