Frequent subgraph excavating method based on graphic processor parallel computing

A graphics processor and frequent subgraph technology, applied in the direction of concurrent instruction execution, machine execution device, resource allocation, etc., can solve the problems of single CPU processor platform with heavy load, large amount of calculation, and huge amount of data

Active Publication Date: 2014-02-05
JIANGXI UNIV OF SCI & TECH
View PDF2 Cites 32 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Frequent subgraph excavating method based on graphic processor parallel computing
  • Frequent subgraph excavating method based on graphic processor parallel computing
  • Frequent subgraph excavating method based on graphic processor parallel computing

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0044] The present invention will be described in detail below in conjunction with specific embodiments.

[0045] Variable definitions:

[0046] data_node[] graph dataset

[0047] graphdata[] structure array (node ​​information in node_msg graph, node_lable node label, edge_x edge vertex x, edge_y edge vertex y, edge_weight edge weight)

[0048] rank_node[] node ranking array

[0049] rank_edge[] edge sorted array

[0050] min_sup minimum support

[0051] sum_count records the total number of frequent changes

[0052] stacksource[] receives and returns frequent subgraph result sets

[0053] ksource[] rightmost extended iterative operation storage

[0054] source stores intermediate calculation values

[0055] tid thread label

[0056] bool_device_dfs (source) device status function, returns whether dfs is complete

[0057] stack[maxlen]dfs traverses the stack

[0058] A frequent subgraph mining method based on GPU parallel computing, its main process is as follows ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06F9/38G06F9/50
Inventor 杨书新谭伟徐彬
Owner JIANGXI UNIV OF SCI & TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap