Highly scalable graph search for the Graph500 benchmark

Abstract

Graph500 is a new benchmark to rank supercomputers with a large-scale graph search problem. We found that the provided reference implementations are not scalable in a large distributed environment. We devised an optimized method based on 2D partitioning and other methods such as communication compression and vertex sorting. Our optimized implementation can handle BFS (Breadth First Search) of a large graph with 236 (68.7 billion vertices) and 240 (1.1 trillion) edges in 10.58 seconds while using 1366 nodes and 16,392 CPU cores. This performance corresponds to 103.9 GE/s. We also studied the performance characteristics of our optimized implementation and reference implementations on a large distributed memory supercomputer with a Fat-Tree-based Infiniband network.