Performance characteristics of Graph500 on large-scale distributed environment

Abstract

Graph500 is a new benchmark for supercomputers based on large-scale graph analysis, which is becoming an important form of analysis in many real-world applications. Graph algorithms run well on supercomputers with shared memory. For the Linpack-based supercomputer rankings, TOP500 reports that heterogeneous and distributed-memory super-computers with large numbers of GPGPUs are becoming dominant. However, the performance characteristics of large-scale graph analysis benchmarks such as Graph500 on distributed-memory supercomputers have so far received little study. This is the first report of a performance evaluation and analysis for Graph500 on a commodity-processor-based distributed-memory supercomputer. We found that the reference implementation “replicated-csr” based on distributed level-synchronized breadth-first search solves a large free graph problem with 231 vertices and 235 edges (approximately 2.15 billon vertices and 34.3 billion edges) in 3.09 seconds with 128 nodes and 3,072 cores. This equates to 11 giga-edges traversed per second. We describe the algorithms and implementations of the reference implementations of Graph500, and analyze the performance characteristics with varying graph sizes and numbers of computer nodes and different implementations. Our results will also contribute to the development of optimized algorithms for the coming exascale machines.