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 ==================== 2024 ------ Coming Soon 2023 ------ * `Intro to Graph Neural Networks with cuGraph-DGL `_ * `GTC 2023 Ask the Experts Q&A `_ * `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 `_ 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 =============== * `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 `_