FlashGraph: Processing Billion-Node Graphs on an Array of Commodity SSDs

TL;DR

FlashGraph enables processing of billion-node graphs on a single server with commodity SSDs by overlapping computation and I/O, achieving near in-memory performance and outperforming distributed engines.

Contribution

The paper introduces a semi-external memory graph engine that efficiently utilizes SSDs and a novel I/O merging technique for high-performance graph processing on a single machine.

Findings

Achieves up to 80% of in-memory performance

Outperforms PowerGraph significantly

Processes billion-node graphs on a single server

Abstract

Graph analysis performs many random reads and writes, thus, these workloads are typically performed in memory. Traditionally, analyzing large graphs requires a cluster of machines so the aggregate memory exceeds the graph size. We demonstrate that a multicore server can process graphs with billions of vertices and hundreds of billions of edges, utilizing commodity SSDs with minimal performance loss. We do so by implementing a graph-processing engine on top of a user-space SSD file system designed for high IOPS and extreme parallelism. Our semi-external memory graph engine called FlashGraph stores vertex state in memory and edge lists on SSDs. It hides latency by overlapping computation with I/O. To save I/O bandwidth, FlashGraph only accesses edge lists requested by applications from SSDs; to increase I/O throughput and reduce CPU overhead for I/O, it conservatively merges I/O requests. These designs maximize performance for applications with different I/O characteristics. FlashGraph exposes a general and flexible vertex-centric programming interface that can express a wide variety of graph algorithms and their optimizations. We demonstrate that FlashGraph in semi-external memory performs many algorithms with performance up to 80% of its in-memory implementation and significantly outperforms PowerGraph, a popular distributed in-memory graph engine.