Paralleling and Accelerating Arc Consistency Enforcement with Recurrent Tensor Computations

TL;DR

This paper introduces a novel parallel arc consistency enforcement method using recurrent tensor operations, significantly improving efficiency on large, densely connected constraint networks by leveraging GPU acceleration.

Contribution

It presents a new paradigm transforming arc consistency enforcement into recurrent tensor computations, enabling full parallelization and rapid convergence.

Findings

Achieves high efficiency on large constraint networks

Fully leverages GPU parallelization

Requires fewer iterations for convergence

Abstract

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.