Paralleling and Accelerating Arc Consistency Enforcement with Recurrent Tensor Computations

Fuente: arXiv
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Main Author: Yang, Mingqi
Format: Preprint
Published: 2024
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author Yang, Mingqi
author_facet Yang, Mingqi
contents We propose a new arc consistency enforcement paradigm that transforms arc consistency enforcement into recurrent tensor operations. In each iteration of the recurrence, all involved processes can be fully parallelized with tensor operations. And the number of iterations is quite small. Based on these benefits, the resulting algorithm fully leverages the power of parallelization and GPU, and therefore is extremely efficient on large and densely connected constraint networks.
format Preprint
id arxiv_https___arxiv_org_abs_2407_11388
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Paralleling and Accelerating Arc Consistency Enforcement with Recurrent Tensor Computations
Yang, Mingqi
Distributed, Parallel, and Cluster Computing
Data Structures and Algorithms
We propose a new arc consistency enforcement paradigm that transforms arc consistency enforcement into recurrent tensor operations. In each iteration of the recurrence, all involved processes can be fully parallelized with tensor operations. And the number of iterations is quite small. Based on these benefits, the resulting algorithm fully leverages the power of parallelization and GPU, and therefore is extremely efficient on large and densely connected constraint networks.
title Paralleling and Accelerating Arc Consistency Enforcement with Recurrent Tensor Computations
topic Distributed, Parallel, and Cluster Computing
Data Structures and Algorithms
url https://arxiv.org/abs/2407.11388