Recently, a new trend has emerged in the field of parallel and high performance computing, the hybrid implementation using CPU-GPU modules. In such implementations, the computational load is shared between the CPU and GPU, in order to improve the computational efficiency. However, the task of sharing the computational load between the two modules is a rather difficult one, with a number of limitations being imposed. This paper extends our recent work on community detection, which is based on transforming a network of nodes into a set of threaded binary trees. In this work, we share the computational load between the two units: the CPU takes specific samples of the network communities and organizes them in the form of threaded binary trees. The GPU takes over the heavy load of reading this data and transforming it into a path-matrix. Finally, this matrix is sent back to the CPU for analysis, community detection and overlaps, as well as network information upgrades. Our simulation results show significant improvement over our previous strategy and other known community detection strategies found in the literature.

中文翻译：

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加权网络中的混合 CPU-GPU 社区检测


最近，并行高性能计算领域出现了一个新趋势，即使用CPU-GPU模块的混合实现。在这样的实现中，计算负载在CPU和GPU之间分担，以提高计算效率。然而，在两个模块之间共享计算负载的任务是一项相当困难的任务，存在许多限制。本文扩展了我们最近在社区检测方面的工作，该工作基于将节点网络转换为一组线程二叉树。在这项工作中，我们在两个单元之间共享计算负载：CPU 获取网络社区的特定样本，并以线程二叉树的形式组织它们。 GPU 承担了读取这些数据并将其转换为路径矩阵的繁重负载。最后，这个矩阵被送回CPU进行分析、社区检测和重叠以及网络信息升级。我们的模拟结果显示比我们之前的策略和文献中发现的其他已知社区检测策略有显着改进。