GridGraph: Large-Scale Graph Processing on a Single Machine Using 2-Level Hierarchical Partitioning

In this paper, we present GridGraph, a system for processing large-scale graphs on a single machine. Grid- Graph breaks graphs into 1D-partitioned vertex chunks and 2D-partitioned edge blocks using a first fine-grained level partitioning in preprocessing. A second coarsegrained level partitioning is applied in runtime. Through a novel dual sliding windows method, GridGraph can stream the edges and apply on-the-fly vertex updates, thus reduce the I/O amount required for computation. The partitioning of edges also enable selective scheduling so that some of the blocks can be skipped to reduce unnecessary I/O. This is very effective when the active vertex set shrinks with convergence.

Our evaluation results show that GridGraph scales seamlessly with memory capacity and disk bandwidth, and outperforms state-of-the-art out-of-core systems, including GraphChi and X-Stream. Furthermore, we show that the performance of GridGraph is even competitive with distributed systems, and it also provides significant cost efficiency in cloud environment.