Frequent subgraph excavating method based on graphic processor parallel computing

Abstract

The invention discloses a frequent subgraph excavating method based on graphic processor parallel computing. The method includes marking out a plurality of thread blocks through a graphic processing unit (GPU), evenly distributing frequent sides to different threads to conduct parallel processing, obtaining different extension subgraphs through right most, returning the graph excavating data set obtained by each thread to each thread block, finally utilizing the GPU to conduct data communication with a memory and returning a result to a central processing unit (CPU) to process the result. The graph excavating method is feasible and effective, graph excavating performance is optimized under intensive large data environment, graph excavating efficiency is improved, data information is provided for scientific research analysis, market research and the like fast and reliably, and a parallel excavating method on a compute unified device architecture (CUDA) is achieved.

Description

technical field [0001] The invention relates to a frequent subgraph mining method based on parallel computing of a graphics processor, so as to improve the efficiency of graph data mining. Background technique [0002] With the continuous deepening and development of the field of data mining, graph data mining has attracted more and more attention from researchers, so graph mining has become a new research direction of data mining and machine learning. This research has great potential value in many fields in real life, such as protein structure analysis in bioinformatics, genome identification, connection between entities in social networks, Web content mining in Web analysis, Web link structure analysis, text information retrieval, etc. [0003] At present, the research work on graph data mining at home and abroad can be mainly divided into the following four categories: 1. Research on graph matching; 2. Research on keyword query in graph data; 3. Research on frequent sub...

AI Technical Summary

Problems solved by technology

There are several drawbacks to this traditional mining method: first, the mining process requires complex isomorphism verification, and the subgraph isomorphism problem is actually an NP-complete problem, and its calculation is complex and computationally intensive; second, in the mining process , a large number of repeated calculations will be performed, wasting resources; third, due to the huge amount of data, the load of the single CPU processor platform is too large, and the internal storage unit of the CPU is not enough to use

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