cuGraph Blogs and Presentations

The RAPIDS team blogs at https://medium.com/rapids-ai, and many of these blog posts provide deeper dives into features from cuGraph. Here, we’ve selected just a few that are of particular interest to cuGraph users:

Blogs & Conferences

2025

Coming Soon

2024

• NVIDIA cuGraph: 500x faster alternate for NetworkX for Graphs

• Revolutionizing Graph Analytics: Next-Gen Architecture with NVIDIA cuGraph Acceleration

• Accelerated, Production-Ready Graph Analytics for NetworkX Users

• NetworkX Introduces Zero Code Change Acceleration Using NVIDIA cuGraph

• NVIDIA cuGraph: Accelerate Graph Analytics with GPUs

• Enhanced Data Analytics: Integrating NVIDIA Rapids cuGraph with TigerGraph

• Insights, Techniques, and Evaluation for LLM-Driven Knowledge Graphs

• Accelerating NetworkX on NVIDIA GPUs for High Performance Graph Analytics

• Introduction to Graph Neural Networks with NVIDIA cuGraph-DGL

• Supercharge Graph Analytics at Scale with GPU-CPU Fusion for 100x Performance

• Introduction to Graph Analysis using cuGraph

2022

• GTC: State of cuGraph (video & slides)

• GTC: Scaling and Validating Louvain in cuGraph against Massive Graphs (video & slides)

• KDD Tutorial on Accelerated GNN Training with DGL/PyG and cuGraph

2021

• GTC 21 - State of RAPIDS cuGraph and what’s comming next

2020

• Status of RAPIDS cuGraph — Refactoring Code And Rethinking Graphs

• Tackling Large Graphs with RAPIDS cuGraph and CUDA Unified Memory on GPUs

• RAPIDS cuGraph adds NetworkX and DiGraph Compatibility

• Large Graph Visualization with RAPIDS cuGraph

• GTC 20 Fall - cuGraph Goes Big

2019

• RAPIDS cuGraph

• RAPIDS cuGraph — The vision and journey to version 1.0 and beyond

• RAPIDS cuGraph : multi-GPU PageRank

• Similarity in graphs: Jaccard versus the Overlap Coefficient

• GTC19 Spring - Accelerating Graph Algorithms with RAPIDS

• GTC19 Fall - Multi-Node Multi-GPU Machine Learning and Graph Analytics with RAPIDS

2018

• GTC18 Fall - RAPIDS: Benchmarking Graph Analytics on the DGX-2

Media

• NetworkX GPU Acceleration with cuGraph in Python <https://www.youtube.com/watch?v=92OxVC-1aiE>

• NVIDIA RAPIDS cuGraph : GPU acceleration for NetworkX, Graph Analytics <https://www.youtube.com/watch?v=FBxAIoH49Xc>

• Accelerating Graph Analysis on GPUs <https://www.youtube.com/watch?v=piNP2LbfMFk>

• Nvidia Rapids cuGraph: Making graph analysis ubiquitous

• RAPIDS cuGraph – Accelerating all your Graph needs

Academic Papers

• Seunghwa Kang, Chuck Hastings, Joe Eaton, Brad Rees cuGraph C++ primitives: vertex/edge-centric building blocks for parallel graph computing

• Alex Fender, Brad Rees, Joe Eaton (2022) Massive Graph Analytics Bader, D. (Editor) CRC Press

• S Kang, A. Fender, J. Eaton, B. Rees. Computing PageRank Scores of Web Crawl Data Using DGX A100 Clusters. In IEEE HPEC, Sep. 2020

• Hricik, T., Bader, D., & Green, O. (2020, September). Using RAPIDS AI to accelerate graph data science workflows. In 2020 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1-4). IEEE.

• Richardson, B., Rees, B., Drabas, T., Oldridge, E., Bader, D. A., & Allen, R. (2020, August). Accelerating and Expanding End-to-End Data Science Workflows with DL/ML Interoperability Using RAPIDS. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3503-3504).

• A Gondhalekar, P Sathre, W Feng Hybrid CPU-GPU Implementation of Edge-Connected Jaccard Similarity in Graph Datasets

Other Blogs

• 4 graph algorithms on steroids for data scientists with cugraph

• Where should I walk

• Where really are the parking spots?

• Accelerating Single Cell Genomic Analysis using RAPIDS

• Running Large-Scale Graph Analytics with Memgraph and NVIDIA cuGraph Algorithms

• Dev Blog Repost: Similarity in Graphs: Jaccard Versus the Overlap Coefficient

RAPIDS Event Notebooks

• KDD 2022 Notebook that demonstates using cuDF for ETL/data cleaning and XGBoost for training a fraud predection model.

• SciPy 22 Notebook comparing cuGraph to NetworkX

• KDD 2020 Tutorial Notebooks - Accelerating and Expanding End-to-End Data Science Workflows with DL/ML Interoperability Using RAPIDS