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README.md

 cuGraph - GPU Graph Analytics

The RAPIDS cuGraph library is a collection of graph analytics that process data found in GPU Dataframes - see cuDF. cuGraph aims to provide a NetworkX-like API that will be familiar to data scientists, so they can now build GPU-accelerated workflows more easily.

For more project details, see rapids.ai.

NOTE: For the latest stable README.md ensure you are on the master branch.

import cugraph

# assuming that data has been loaded into a cuDF (using read_csv) Dataframe
# create a Graph using the source (src) and destination (dst) vertex pairs the GDF  
G = cugraph.Graph()
G.add_edge_list(gdf["src"], gdf["dst"])

# Call cugraph.pagerank to get the pagerank scores
gdf_page = cugraph.pagerank(G)

for i in range(len(gdf_page)):
	print("vertex " + str(gdf_page['vertex'][i]) + 
		" PageRank is " + str(gdf_page['pagerank'][i]))  

Supported Algorithms:

Algorithm Scale Notes
PageRank Single-GPU
Personal PageRank Single-GPU
Jaccard Similarity Single-GPU
Weighted Jaccard Single-GPU
Overlap Similarity Single-GPU
SSSP Single-GPU Updated to provide path info
BSF Single-GPU
Triangle Counting Single-GPU
Subgraph Extraction Single-GPU
Spectral Clustering - Balanced-Cut Single-GPU
Spectral Clustering - Modularity Maximization Single-GPU
Louvain Single-GPU
Renumbering Single-GPU
Basic Graph Statistics Single-GPU
Weakly Connected Components Single-GPU

cuGraph 0.8 Notice

cuGraph version 0.8 has some limitations:

  • Only Int32 Vertex ID are supported
  • Only float (FP32) edge data is supported
  • Vertex numbering is assumed to start at zero

These limitations are being addressed and will be fixed soon.

Getting cuGraph

Intro

There are 3 ways to get cuGraph :

  1. Quick start with Docker Demo Repo
  2. Conda Installation
  3. Build from Source

Quick Start

Please see the Demo Docker Repository, choosing a tag based on the NVIDIA CUDA version you’re running. This provides a ready to run Docker container with example notebooks and data, showcasing how you can utilize all of the RAPIDS libraries: cuDF, cuML, and cuGraph.

Conda

It is easy to install cuGraph using conda. You can get a minimal conda installation with Miniconda or get the full installation with Anaconda.

Install and update cuGraph using the conda command:

# CUDA 9.2
conda install -c nvidia -c rapidsai -c numba -c conda-forge -c defaults cugraph cudatoolkit=9.2

# CUDA 10.0
conda install -c nvidia -c rapidsai -c numba -c conda-forge -c defaults cugraph cudatoolkit=10.0

Note: This conda installation only applies to Linux and Python versions 3.6/3.7.

Build from Source and Contributing

Please see our guide for building and contributing to cuGraph.

Documentation

Python API documentation can be generated from docs directory.


Open GPU Data Science

The RAPIDS suite of open source software libraries aim to enable execution of end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposing that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.

Apache Arrow on GPU

The GPU version of Apache Arrow is a common API that enables efficient interchange of tabular data between processes running on the GPU. End-to-end computation on the GPU avoids unnecessary copying and converting of data off the GPU, reducing compute time and cost for high-performance analytics common in artificial intelligence workloads. As the name implies, cuDF uses the Apache Arrow columnar data format on the GPU. Currently, a subset of the features in Apache Arrow are supported.

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