  The BFS algorithm is a basic graph data processing algorithm and many other
graph data processing algorithms have similar architectural features with BFS
algorithm and can be built on the basis of BFS algorithm model. We analyze the
differences between graph algorithms and traditional high-performance
algorithms in detail, propose a new way of classifying algorithms into data
independent algorithm and data correlation algorithm based on their run-time
correlation with data, and use this new classification to explain the validity
of the methods proposed in this paper. Through a deeper analysis of graph data,
we propose a new fundamental perspective on understanding graph data,
establishing a link between two basic data structures, graph and tree, and
viewing graph data as consisting of smaller subgraphs and edge trees. Small
degree vertices are found to be one of important cause of random memory access.
Based on this, we propose a general, easy to implement, and efficient method
for graph data processing, with the basic idea of treating low-degree vertices
and core subgraphs separately, thus significantly reducing the size of random
memory access and improving the efficiency of memory access. Finally, we
evaluated the performance of the method on three major data center computing
platforms (Intel, AMD, and ARM), and the experiments showed that it brought
19.7%, 31.8% and 17.9% performance improvement, respectively, with a
performance-power ratio of 282.70 MTEPS/s on the ARM platform, ranking it among
the Green graph500 in November 2019. World No. 1 on the big dataset list.
