---
title: "Gunrock — paper by Wang et al. (2015) · PPoPP 2016"
description: "Gunrock instead builds everything around the frontier , the set of vertices or edges active in the… 37 references, cited by 81 papers here."
authors: ["Yangzihao Wang", "Andrew Davidson", "Yuechao Pan", "Yuduo Wu", "Andy Riffel", "John D. Owens"]
year: 2015
venue: "PPoPP 2016"
source: "https://arxiv.org/abs/1501.05387"
doi: "10.1145/2851141.2851145"
arxiv: "1501.05387"
licence: "CC BY 4.0 (arXiv OAI)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2015-gunrock-high-performance-graph-processing/"
---
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# Gunrock: A High-Performance Graph Processing Library on the GPU

**Authors:** [Yangzihao Wang](https://md.research.elamur.ai/free/en/authors/yangzihao-wang/), Andrew Davidson, Yuechao Pan, Yuduo Wu, Andy Riffel and John D. Owens · **Year:** 2015 · **Venue:** PPoPP 2016 · **Licence:** [CC BY 4.0 (arXiv OAI)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://doi.org/10.1145/2851141.2851145) · [PDF](https://arxiv.org/pdf/1501.05387) · [DOI 10.1145/2851141.2851145](https://doi.org/10.1145/2851141.2851145) · [arXiv 1501.05387](https://arxiv.org/abs/1501.05387) · [OpenAlex](https://openalex.org/W2951135776)

**Citations:** 338 citations (OpenAlex, 2026-10-14) · 81 in our library, of the 149,439 papers whose bibliographies we read

## Summary
Graph analytics maps poorly onto GPUs. Graph data leads to irregular memory access and branching, and fast GPU code usually means writing hand-tuned "hardwired" kernels for each algorithm. Gunrock is an open-source, bulk-synchronous graph library for a single GPU that tries to offer both speed and ease of programming. Most earlier GPU frameworks borrowed CPU models such as GAS or Pregel-style messaging. Gunrock instead builds everything around the *frontier*, the set of vertices or edges active in the current step. Programmers write primitives as operations that either compute on a frontier or produce a new one. Load balancing, kernel fusion, push-pull (direction-optimal) traversal, idempotent traversal and priority queues are handled inside the library.

The authors evaluate BFS, SSSP, BC, CC and PageRank on six graphs. Gunrock is typically at least ten times faster than BGL and PowerGraph, roughly matches hardwired GPU code, and beats other high-level GPU libraries. This makes high-performance GPU graph processing practical without deep GPU expertise.

## Key points
- Five primitives are evaluated: BFS, SSSP, BC, CC and PageRank.
- Comparisons cover BGL (Boost), PowerGraph, Ligra, CuSha, MapGraph and hardwired GPU implementations.
- The six test graphs are h09, i04, kron, rgg, roadnet and soc.
- Load balancing is measured by warp execution efficiency (Table 4):
  - For PageRank, Gunrock stays near 99.4–99.6% across graphs, while CuSha ranges from about 63% to 91%.
  - Some competing frameworks ran out of GPU memory on several inputs.
- A new primitive needs only a few hundred lines of code. The project was motivated by the authors spending two months on a single hardwired primitive.
- Limitations stated by the authors:
  - Single-GPU only, and the graph must fit in device memory.
  - Kernel fusion still lags behind hardwired code, and this is the biggest remaining performance gap.
  - General dynamic graph support remains open.

## Interesting ideas
- **Organise around the active data, not the computation steps.** Making "the set of items currently in play" the core object lets one abstraction cover many iterative algorithms. It also leaves room for optimisations underneath. The same design could suit iterative processing over other networks, such as trade-flow graphs or road and settlement networks.
- **Separate "what" from "how".** Users declare the operation on the frontier, and the library chooses how to distribute the work (fine- vs. coarse-grained mapping). Hiding performance-critical scheduling behind a small API lets non-experts get near-expert speed in any compute-heavy toolkit.
- **Switch strategy by frontier size.** Gunrock adopts Beamer et al.'s direction-optimal idea: alternate between pushing from the frontier and pulling toward it, depending on how large the frontier is. Choosing the algorithm at runtime from a cheap statistic is a widely transferable trick.
- **Measure load balance directly.** Reporting warp execution efficiency, the share of threads actually busy, shows why a framework is fast or slow, not just that it is. Other parallel pipelines could expose a similar utilisation metric alongside runtime.
- **Sample the frontier for a rough start.** The authors propose, as future work, taking a random subset of the frontier to compute an approximate or seeded solution before running on the full graph. This could speed up convergence of iterative methods on large data.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of California, Davis](https://md.research.elamur.ai/free/en/universities/university-of-california-davis/) — University, Davis, United States · 34 works · 42,940 citations

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🧭 Topics (1): [⚡ GPU & parallel computing](https://md.research.elamur.ai/free/en/topics/gpu-parallel-computing/) · 41 works

## Cite

Cite as: Yangzihao Wang, Andrew Davidson, Yuechao Pan, Yuduo Wu, Andy Riffel and John D. Owens. 2015. Gunrock: A High-Performance Graph Processing Library on the GPU. PPoPP 2016.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wang2015gunrock,
  title = {{Gunrock: A High-Performance Graph Processing Library on the GPU}},
  author = {Yangzihao Wang, Andrew Davidson, Yuechao Pan, Yuduo Wu, Andy Riffel and John D. Owens},
  booktitle = {PPoPP 2016},
  year = {2015},
  doi = {10.1145/2851141.2851145},
  eprint = {1501.05387},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1145/2851141.2851145}
}
```

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## References

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*Updated 2 November 2026*