An important combinatorial problem is subgraph isomorphism, which formalizes the task of searching for occurrences of a known substructure within a larger structure represented by a graph: applications are in the fields of chemistry, biology, medicine, databases, social network analysis. Subgraph isomorphism has been proven to be NP-complete in the general case, but several algorithms exist that use heuristics to achieve an affordable run time for common classes of graphs. The need of working with larger and larger graphs makes attractive the idea of parallelizing this task; however, a consensus has not yet been reached on what is the best strategy for doing so.

In this paper, we present two versions of a new, parallel algorithm that is based on a re-design of the well known VF3 algorithm. We discuss the changes that were made to efficiently distribute the work among multiple processors. The algorithms have been evaluated with a comprehensive experimentation, using several publicly available graph datasets, to demonstrate their effectiveness in exploiting the parallelism.

一个重要的组合问题是子图同构，它使在图表示的较大结构中搜索已知子结构的出现的任务形式化：应用程序在化学，生物学，医学，数据库，社交网络分析领域。在一般情况下，子图同构已被证明是NP完全的，但是存在几种使用启发式算法为常见图类提供可承受运行时间的算法。需要使用越来越大的图形使将任务并行化的想法很有吸引力。但是，关于什么是最佳策略尚未达成共识。

在本文中，我们介绍了两种新版本的并行算法，它们基于众所周知的VF3算法的重新设计。我们讨论为有效地在多个处理器之间分配工作而进行的更改。已经使用几个可公开获得的图形数据集通过综合实验对算法进行了评估，以证明算法在利用并行性方面的有效性。