cugraph

PyGraphistry is a Python library to quickly load, shape, embed, and explore big graphs with the GPU-accelerated Graphistry visual graph analyzer

Unlimited Data-Science Benchmarks for Numeric, Tabular and Graph Workloads

The Incredible RAPIDS: a curated list of tutorials, papers, projects, communities and more relating to RAPIDS.

cuGraph Docs - RAPIDS Graph Analytics Documentation

An interactive visualization of all of English Wikipedia.

Make AI coding agents write RAPIDS-first GPU Python.

A centralized benchmarking suite to facilitate comparative profiling of tools across graph analytic libraries and datasets

How to use GPU-accelerated tools to conduct data science faster, leading to more scalable, reliable, and cost-effective results.

GPU-accelerated road network graph analytics pipeline — SSSP, 5-metric centrality analysis, and nx-cugraph backend methods, inspired by NVIDIA DLI. / NVIDIA DLI 학습 기반 GPU 가속 도로망 그래프 분석 파이프라인 — SSSP · 중심성 5종 · nx-cugraph 백엔드 3방식 구현.

Decentralised self-organization framework for UAV swarms, powered by NVIDIA RAPIDS cuGraph.

Harness the Chandelier — connect to the center of your conversation.

CUDA 13 graph AI examples using cuGraph, PyTorch Geometric, GraphSAGE, transaction graphs, and reproducible CPU/GPU workflows.

The RAPIDS cuGraph library is a collection of GPU accelerated graph algorithms that process data found in GPU DataFrames.

Analysis of Bitcoin transactions with Pandas, PySpark, cuDF, cuGraph: trends, classification, and clustering.