Exploiting GPUs in Solving (Distributed) Constraint Optimization Problems with Dynamic Programming

Ferdinando Fioretto, Tiep Le, Enrico Pontelli, William Yeoh, Tran Cao Son

Abstract: This paper proposes the design and implementation of a dynamic programming based algorithm for (distributed) constraint optimization, which exploits modern massively parallel architectures, such as those found in modern Graphical Processing Units (GPUs). The paper studies the proposed algorithm in both centralized and distributed optimization contexts. The experimental analysis, performed on unstructured and structured graphs, shows the advantages of employing GPUs, resulting in enhanced performances and scalability.