From f58be5e142b2c9cc6d4f2914c00d8687af794618 Mon Sep 17 00:00:00 2001 From: acostadon Date: Fri, 9 Feb 2024 10:36:27 -0500 Subject: [PATCH] first changes to docs blogs and nx docs --- docs/cugraph/source/basics/index.rst | 1 + docs/cugraph/source/basics/nx_transition.rst | 12 +++++++----- docs/cugraph/source/tutorials/cugraph_blogs.rst | 15 +++++++++++++++ 3 files changed, 23 insertions(+), 5 deletions(-) diff --git a/docs/cugraph/source/basics/index.rst b/docs/cugraph/source/basics/index.rst index 7bba301b657..9693db94da0 100644 --- a/docs/cugraph/source/basics/index.rst +++ b/docs/cugraph/source/basics/index.rst @@ -8,4 +8,5 @@ Basics cugraph_intro nx_transition + nx_cuGraph_algos cugraph_cascading diff --git a/docs/cugraph/source/basics/nx_transition.rst b/docs/cugraph/source/basics/nx_transition.rst index 3d116162c09..e7fc02d8315 100644 --- a/docs/cugraph/source/basics/nx_transition.rst +++ b/docs/cugraph/source/basics/nx_transition.rst @@ -1,9 +1,8 @@ ************************************** -NetworkX Compatibility and Transition +NetworkX by calling cuGraph Algorithms ************************************** -*Note: this is a work in progress and will be updatred and changed as we better flesh out -compatibility issues* +*Note: This behavior is still supported but will soon be deprecated. Going forward, using nx_cugraph as a NetworkX backend will be the the primary method.* One of the goals of RAPIDS cuGraph is to mimic the NetworkX API to simplify the transition to accelerated GPU data science. However, graph analysis, @@ -18,8 +17,11 @@ But sometimes it is easier to replace just a portion. Last Update ########### -Last Update: Oct 14th, 2020 -Release: 0.16 +Last Update: February 7th, 2024 +Release: 24.04 + +**CuGraph is now a registered backend for networkX. This is described in the blog on +`Accelerating NetworkX on NVIDIA GPUs for High Performance Graph Analytics`** Information on `NetworkX `_ diff --git a/docs/cugraph/source/tutorials/cugraph_blogs.rst b/docs/cugraph/source/tutorials/cugraph_blogs.rst index 368dbcce4f8..3db8387e393 100644 --- a/docs/cugraph/source/tutorials/cugraph_blogs.rst +++ b/docs/cugraph/source/tutorials/cugraph_blogs.rst @@ -9,6 +9,17 @@ 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) `_ @@ -50,6 +61,8 @@ Media 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 @@ -58,6 +71,8 @@ Academic Papers * 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 ========================