摘要
Graph analysis can be done at scale by using Spark GraphX which loading data into memory and running graph analysis in parallel.In this way,we should take data out of graph databases and put it into memory.Considering the limitation of memory size,the premise of accelerating graph analytical process reduces the graph data to a suitable size without too much loss of similarity to the original graph.This paper presents our method of data cleaning on the software graph.We use SEQUITUR data compression algorithm to find out hot code path and store it as a whole paths directed acyclic graph.Hot code path is inherent regularity of a program.About 10 to 200 hot code path account for 40%-99%of a program’s execution cost.These hot paths are acyclic contribute more than 0.1%-1.0%of some execution metric.We expand hot code path to a suitable size which is good for runtime and keeps similarity to the original graph.
基金
This research work is supported by Hunan Provincial Education Science 13th Five-Year Plan(Grant No.XJK016BXX001)
Social Science Foundation of Hunan Province(Grant No.17YBA049)
Hunan Provincial Natural Science Foundation of China(Grant No.2017JJ2016)
The work is also supported by Open foundation for University Innovation Platform from Hunan Province,China(Grand No.16K013)
the 2011 Collaborative Innovation Center of Big Data for Financial and Economical Asset Development and Utility in Universities of Hunan Province.National Students Platform for Innovation and Entrepreneurship Training(Grand No.201811532010).
