Whereas previous versions of cuGraph have included mechanisms to make it trivial to plug in cuGraph algorithm calls. Beginning with version 24.02, nx-cuGraph is now a networkX backend. The user now need only install nx-cugraph to experience GPU speedups.

Lets look at some examples of algorithm speedups comparing CPU based NetworkX to dispatched versions run on GPU with nx_cugraph.

Each chart has three measurements.

  • NX - running the algorithm natively with networkX on CPU.

  • nx-cugraph - running with GPU accelerated networkX achieved by simply calling the cugraph backend. This pays the overhead of building the GPU resident object for each algorithm called. This achieves significant improvement but stil isn’t compleltely optimum.

  • nx-cugraph (preconvert) - This is a bit more complicated since it involves building (precomputing) the GPU resident graph ahead and reusing it for each algorithm.

Ancestors BFS Tree Connected Components Descendents Katz Pagerank Single Source Shortest Path Weakly Connected Components

Command line example#

Open bc_demo.ipy and paste the code below.

import pandas as pd
import networkx as nx

url = "https://data.rapids.ai/cugraph/datasets/cit-Patents.csv"
df = pd.read_csv(url, sep=" ", names=["src", "dst"], dtype="int32")
G = nx.from_pandas_edgelist(df, source="src", target="dst")

%time result = nx.betweenness_centrality(G, k=10)

Run the command:

user@machine:/# ipython bc_demo.ipy

You will observe a run time of approximately 7 minutes…more or less depending on your cpu.

Run the command again, this time specifiying cugraph as the NetworkX backend of choice.

user@machine:/# NETWORKX_BACKEND_PRIORITY=cugraph ipython bc_demo.ipy

This run will be much faster, typically around 20 seconds depending on your GPU.

user@machine:/# NETWORKX_BACKEND_PRIORITY=cugraph ipython bc_demo.ipy

There is also an option to add caching. This will dramatically help performance when running multiple algorithms on the same graph.


When running Python interactively, cugraph backend can be specified as an argument in the algorithm call.

For example:

nx.betweenness_centrality(cit_patents_graph, k=k, backend="cugraph")

The latest list of algorithms that can be dispatched to nx-cuGraph for acceleration is found here.