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

arXiv: https://arxiv.org/abs/1408.0500 · HTML: https://mlsanity.com/paper/1408.0500/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2014.

## Built on by (most cited first, 5 of 5)

- [Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads](https://mlsanity.com/paper/2105.11118/) (2021)
- [Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching](https://mlsanity.com/paper/2208.09151/) (2022)
- [LPS-GNN : Deploying Graph Neural Networks on Graphs with 100-Billion Edges](https://mlsanity.com/paper/2507.14570/) (2025)
- [A Learning-based Approach Towards Automated Tuning of SSD Configurations](https://mlsanity.com/paper/2110.08685/) (2021)
- [Accelerating Storage-Based Training for Graph Neural Networks](https://mlsanity.com/paper/2601.01473/) (2026)

## Abstract (arXiv)

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.