okoskinen / cugraph

cuGraph

 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.