细粒度混合CPU-GPU介数中心计算算法
A Fine-Grained Hybrid CPU-GPU Algorithm for Betweenness Centrality Computations
摘要：
介数中心性（BC）是大规模图的一个重要的图分析应用。虽然有许多努力在多核CPU和众核GPU上并行化介数中心算法，在这项工作中，我们提出了一种新的细粒度CPU-GPU混合算法，将图形划分为CPU和GPU分区，并同时在CPU和GPU资源上执行BC计算，同时具有非常少量的CPU-GPU通信。在我们的混合BC算法的正向阶段利用BC问题中固有的多源属性。我们还执行了一个新的混合和异步向后阶段，执行最小的CPU-GPU同步。使用大量具有不同特征的图进行的评估表明，我们的混合方法比基于流行的批量同步范式（BSP）方法的现有混合算法在性能上提高了80%，并且CPU-GPU通信减少了80 - 90%。
英文摘要：
Betweenness centrality (BC) is an important graph analytical application for large-scale graphs. While there are many efforts for parallelizing betweenness centrality algorithms on multi-core CPUs and many-core GPUs, in this work, we propose a novel fine-grained CPU-GPU hybrid algorithm that partitions a graph into CPU and GPU partitions, and performs BC computations for the graph on both the CPU and GPU resources simultaneously with very small number of CPU-GPU communications. The forward phase in our hybrid BC algorithm leverages the multi-source property inherent in the BC problem. We also perform a novel hybrid and asynchronous backward phase that performs minimal CPU-GPU synchronizations. Evaluations using a large number of graphs with different characteristics show that our hybrid approach gives 80% improvement in performance, and 80-90% less CPU-GPU communications than an existing hybrid algorithm based on the popular Bulk Synchronous Paradigm (BSP) approach.
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