HPC-gSpan:基于fpga的频繁子图挖掘并行系统

2014 24th International Conference on Field Programmable Logic and Applications (FPL) Pub Date : 2014-10-20 DOI:10.1109/FPL.2014.6927441

Athanasios Stratikopoulos, Grigorios Chrysos, I. Papaefstathiou, A. Dollas

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引用次数: 11

摘要

图挖掘是数据挖掘领域的一个重要研究领域。频繁子图挖掘是图挖掘中最具挑战性的任务之一。这项工作提出了第一个基于fpga的实现，据我们所知，对于频繁子图挖掘(FSM)问题，即gSpan，最有效和最知名的算法。该系统被命名为高性能计算- gspan (HPC-gSpan)，当在高端CPU上执行各种实际数据集时，与gboost库的官方软件解决方案相比，实现了多倍的加速。

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HPC-gSpan: An FPGA-based parallel system for frequent subgraph mining

Graph mining is an important research area within the domain of data mining. One of the most challenging tasks of graph mining is frequent subgraph mining. This work presents the first FPGA-based implementation, to the best of our knowledge, of the most efficient and well-known algorithm for the Frequent Subgraph Mining (FSM) problem, i.e. gSpan. The proposed system, named High Performance Computing-gSpan (HPC-gSpan), achieves manyfold speedup vs. the official software solution of the gboost library when executed on a high-end CPU for various real-world datasets.

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2014 24th International Conference on Field Programmable Logic and Applications (FPL)

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