Discrete optimization is a central problem in artificial intelligence. The optimization of the aggregated cost of a network of cost functions arises in a variety of problems including Weighted Constraint Programs (WCSPs), Distributed Constraint Optimization (DCOP), as well as optimization in stochastic variants such as the tasks of finding the most probable explanation (MPE) in belief networks. Inference-based algorithms are powerful techniques for solving discrete optimization problems, which can be used independently or in combination with other techniques. However, their applicability is often limited by their compute intensive nature and their space requirements. This paper proposes the design and implementation of a novel inference-based technique, which exploits modern massively parallel architectures, such as those found in Graphical Processing Units (GPUs), to speed up the resolution of exact and approximated inference-based algorithms for discrete optimization. The paper studies the proposed algorithm in both centralized and distributed optimization contexts. The paper demonstrates that the use of GPUs provides significant advantages in terms of runtime and scalability, achieving up to two orders of magnitude in speedups and showing a considerable reduction in execution time (up to 345 times faster) with respect to a sequential version.

中文翻译：

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使用GPU加速（分布式）离散优化的精确和近似推断

离散优化是人工智能的核心问题。成本函数网络的总成本的优化产生于各种问题，包括加权约束程序（WCSP），分布式约束优化（DCOP）以及随机变量的优化，例如寻找最可能的解释的任务（MPE）在信念网络中。基于推理的算法是解决离散优化问题的强大技术，可以独立使用或与其他技术结合使用。但是，它们的适用性通常受其计算密集型性质和空间要求的限制。本文提出了一种新颖的基于推理的技术的设计和实现，该技术利用了现代大规模并行体系结构（例如在图形处理单元（GPU）中发现的那些体系结构），以加快针对离散优化的精确和近似基于推理的算法的分辨率。 。本文在集中式和分布式优化环境中研究了该算法。本文证明了GPU的使用在运行时和可伸缩性方面具有明显优势，