In practice symmetries of combinatorial structures are computed by transforming the structure into an annotated graph whose automorphisms correspond exactly to the desired symmetries. An automorphism solver is then employed to compute the automorphism group of the constructed graph. Such solvers have been developed for over 50 years, and highly efficient sequential, single core tools are available. However no competitive parallel tools are available for the task. We introduce a new parallel randomized algorithm that is based on a modification of the individualization-refinement paradigm used by sequential solvers. The use of randomization crucially enables parallelization. We report extensive benchmark results that show that our solver is competitive to state-of-the-art solvers on a single thread, while scaling remarkably well with the use of more threads. This results in order-of-magnitude improvements on many graph classes over state-of-the-art solvers. In fact, our tool is the first parallel graph automorphism tool that outperforms current sequential tools.
翻译:在实际操作中,组合结构的对称性是通过将结构转换成一个附加说明的图表来计算的,其自动形态与理想的对称完全对应。 然后,将使用一个自动形态解析器来计算构建的图形的自成一体组。 这些解析器已经开发了50多年,并且具有高度高效的顺序、单一核心工具。 然而,没有竞争的平行工具可用于这项任务。 我们引入了一个新的平行随机化算法, 其依据是修改顺序解析器使用的个性化- 精度模式。 使用随机化非常关键地使得平行化。 我们报告广泛的基准结果, 显示我们的解析器在单线上对最先进的解析器具有竞争力, 而与更多线索的使用相比, 缩放得非常出色。 这的结果是, 许多图形级的测算法改进了州级的解析器。 事实上, 我们的工具是第一个平行的图形自成形工具, 超越了当前顺序工具。