# gunrock/gunrock

Programmable CUDA/C++ GPU Graph Analytics

Repository: https://github.com/gunrock/gunrock
Canonical: https://ross.abutalabs.com/products/gunrock
Homepage: https://gunrock.github.io/gunrock/
Language: C++
License: Apache-2.0
License Family: permissive
Topics: gunrock, cuda, graph-processing, graph-analytics, gpu, graph-primitives, graph-engine, hpc, cxx, algorithms, cpp, essentials, graph, graph-algorithms, gnn, graph-neural-networks, algorithm, parallel-computing, sparse-matrix
Last push: 2026-02-28T17:51:48+00:00

## Health v2 (maintenance only)
Score: 63/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 69, release rhythm 34, longevity 100
- inputs: {"age_days": 4686, "days_push": 186, "days_rel": 228, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1095, forks 228 (observed 2026-08-28T04:03:34.228412+00:00)

## What it is
Gunrock is a CUDA/C++ library for GPU-accelerated graph analytics built on a high-level, data-centric abstraction over vertex and edge frontiers. It lets developers implement graph primitives that scale from one to many GPUs with minimal GPU programming knowledge, and now also supports AMD GPUs via ROCm/HIP.

## Use cases
- run graph algorithms like BFS, PageRank, and SSSP on GPUs
- implement custom graph primitives with a high-level GPU programming model
- accelerate graph neural network workloads on NVIDIA or AMD GPUs
- process large sparse graphs in parallel across multiple GPUs
- prototype graph analytics without writing low-level CUDA kernels

## When to choose
- you need high-performance graph analytics on GPU hardware
- you want to develop new graph primitives quickly with minimal CUDA expertise
- you need multi-GPU scaling within a node
- you want a library that supports both NVIDIA CUDA and AMD ROCm backends

## When to avoid
- you only need CPU-based graph processing
- your graphs are small enough that GPU overhead outweighs gains
- you need a turnkey graph database rather than a programmable analytics library
- you cannot use C++17 or a modern CUDA/ROCm toolchain

## Facets
- artifact type: library
- maturity: active
- function: graphics, math, machine-learning, developer-tools
- domain: graph-processing, gpu-computing, machine-learning
- platform: cpp, windows
- tags: cuda, graph-analytics, gpu-computing, graph-algorithms, parallel-computing, graph-neural-networks, sparse-matrix, hpc, rocm, hip, high-performance-computing, algorithms, gpu, linux, macos

## Member repositories
- gunrock/gunrock (main) score 63

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:34.228412+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:47:15.422683+00:00, confidence not recorded.
  - readme: https://github.com/gunrock/gunrock (fetched 2026-08-28T04:03:34.228412+00:00, sha 3a6f7b2e3310)
  - homepage: https://gunrock.github.io/gunrock/ (fetched 2026-08-29T12:50:09.084739+00:00, sha 6b98e09923d4)
- Data as of 2026-08-30T08:39:29.467469+00:00.