mlsanityData to 14 Oct 2026

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

arXiv 1501.05387 ↗First posted 22 Jan 2015cs.DCcited by 14

Not on the map. Why?

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.

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

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 4; 2023: 3; 2024: 2; 2025: 1; 2026: 1.

14 papers.

Sort:

By area:

  1. 2021Dive into Deep Learningcited by 111
  2. 2024SpecExec: Massively Parallel Speculative Decoding for Interactive LLM Inference on Consumer Devicescited by 46
  3. 2022Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysiscited by 29
  4. 2023The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Surveycited by 14
  5. 2021GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategycited by 8
  6. 2025Enforcing convex constraints in Graph Neural Networkscited by 2
  7. 2022Analyzing the Traffic of MANETs using Graph Neural Networkscited by 1
  8. 2023AdaptGear: Accelerating GNN Training via Adaptive Subgraph-Level Kernels on GPUscited by 1
  9. 2026A Fully GPU-Accelerated Framework for High-Performance Configuration Interaction Selection with Neural Network Quantum Statescited by 1
  10. 2021SPA-GCN: Efficient and Flexible GCN Accelerator with an Application for Graph Similarity Computationcited by 0
  11. 2022Accelerating Backward Aggregation in GCN Training with Execution Path Preparing on GPUscited by 0
  12. 2022gSuite: A Flexible and Framework Independent Benchmark Suite for Graph Neural Network Inference on GPUscited by 0
2 more
  1. 2023Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Designcited by 0
  2. 2024GeoT: Tensor Centric Library for Graph Neural Network via Efficient Segment Reduction on GPUcited by 0