Total: 1
As graphs continue to grow to have billions of vertices and edges, the attention of graph processing is shifted from in-memory graph system to external graph system. Of the two the latter offers a cost-effective option for processing large-scale graphs on a single machine by holding the enormous graph data in both memory and storage. Although modern external graph systems embrace many advanced I/O optimization techniques and can perform well in general, graph algorithms that build upon Breadth-First Search (BFS) (a.k.a. BFS-like algorithms) still commonly suffer poor processing performance. The key reason is that the recursive vertex traversal nature of BFS may lead to poor I/O efficiency in loading the required graph data from storage for processing. Thus, this paper presents I/O-Efficient Graph Ordering (IOE-Order) to pre-process the graph data, while better I/O efficiency in loading storage-resident graph data can be delivered at runtime to boost the processing performance of BFS-like algorithms. Particularly, IOE-Order comprises two major pre-processing steps. The first is Breadth-First Degree-Second (BFDS) Ordering, which exploits both graph traversal pattern and degree information to store the vertices and edges which are most likely to be accessed together for I/O efficiency improvement. The second is Out-Degree Binning, which splits the BFDS-ordered graph into multiple sorted bins based on out-degrees of vertices so as to 1) further increase I/O-efficiency for runtime graph processing and 2) deliver high flexibility in pre-caching vertices based on the memory availability. In contrast to the state-of-the-art pre-processing techniques for BFS-like algorithms, IOE-Order demonstrates better efficiency and practicability: It delivers higher processing performance by achieving higher I/O efficiency but entails much lower pre-processing overhead.