# Gunrock: A High-Performance Graph Processing Library on the GPU

arXiv: https://arxiv.org/abs/1501.05387 · HTML: https://mlsanity.com/paper/1501.05387/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2015.

## Built on by (most cited first, 14 of 14)

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- [AdaptGear: Accelerating GNN Training via Adaptive Subgraph-Level Kernels on GPUs](https://mlsanity.com/paper/2305.17408/) (2023)
- [A Fully GPU-Accelerated Framework for High-Performance Configuration Interaction Selection with Neural Network Quantum States](https://mlsanity.com/paper/2604.15768/) (2026)
- [SPA-GCN: Efficient and Flexible GCN Accelerator with an Application for Graph Similarity Computation](https://mlsanity.com/paper/2111.05936/) (2021)
- [Accelerating Backward Aggregation in GCN Training with Execution Path Preparing on GPUs](https://mlsanity.com/paper/2204.02662/) (2022)
- [gSuite: A Flexible and Framework Independent Benchmark Suite for Graph Neural Network Inference on GPUs](https://mlsanity.com/paper/2210.11601/) (2022)
- [Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Design](https://mlsanity.com/paper/2308.08174/) (2023)
- [GeoT: Tensor Centric Library for Graph Neural Network via Efficient Segment Reduction on GPU](https://mlsanity.com/paper/2404.03019/) (2024)

## Abstract (arXiv)

For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and the complexity of programming GPUs have been two significant challenges for developing a programmable high-performance graph library. "Gunrock", our graph-processing system designed specifically for the GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on operations on a vertex or edge frontier. Gunrock achieves a balance between performance and expressiveness by coupling high performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. We evaluate Gunrock on five key graph primitives and show that Gunrock has on average at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives, and better performance than any other GPU high-level graph library.