XTree: Traversal-Based Partitioning for Extreme-Scale Graph Processing on Supercomputers

Abstract

Graph algorithms, such as Breadth First Search (BFS), Single Source Shortest Path (SSSP), PageRank (PR), and Connected Components (CC), are increasingly important in big data processing and analytics. As graph scales (numbers of vertices and edges) have increased from billions to trillions, Supercomputers have huge numbers (up to hundreds of thousands) of computing nodes (CNs) that can provide ultra-high aggregate computing power and memory capacity, thus being particularly suitable for processing extreme-scale graphs with trillions of vertices and edges. However, existing cluster-based graph-parallel systems perform poorly when deployed on supercomputers, since their partitioning methods overlook the hierarchical nature of supercomputer networks and incur prohibitive communication storm. This paper presents XTree, an efficient traversal-based partitioning method for minimizing communication overhead of graph processing on supercomputers. We observe that supercomputers' huge numbers of CNs are usually organized into hierarchical communication domains, which can be modeled as a domain tree where communication in lower-level domains is significantly faster than that in higher-level ones. Therefore, the key idea of XTree's partitioning is to exploit hierarchical locality by viewing the graph as a BFS tree and leveraging the topology knowledge to map the graph's BFS tree onto the domain tree, We evaluate the effectiveness of XTree by running various graph algorithms, on both real-world big graphs and synthetic trillion-scale graphs. XTree substantially reduces communication overhead and achieves orders of magnitude speedup against the Graph500 reference implementations with the state-of-the-art 2D-decomposition partitioning.