Multi-GPU with cuGraph

cuGraph supports multi-GPU leveraging Dask. Dask is a flexible library for parallel computing in Python which makes scaling out your workflow smooth and simple. cuGraph also uses other Dask-based RAPIDS projects such as dask-cuda. The maximum graph size is currently limited to 2 Billion vertices (to be waived in the next versions).

Distributed graph analytics

The current solution is able to scale across multiple GPUs on multiple machines. Distributing the graph and computation lets you analyze datasets far larger than a single GPU’s memory.

With cuGraph and Dask, whether you’re using a single NVIDIA GPU or multiple nodes, your RAPIDS workflow will run smoothly, intelligently distributing the workload across the available resources.

If your graph comfortably fits in memory on a single GPU, you would want to use the single-GPU version of cuGraph. If you want to distribute your workflow across multiple GPUs and have more data than you can fit in memory on a single GPU, you would want to use cuGraph’s multi-GPU features.


from dask.distributed import Client, wait
from dask_cuda import LocalCUDACluster
import cugraph.comms as Comms
import cugraph.dask as dask_cugraph

cluster = LocalCUDACluster()
client = Client(cluster)

# Helper function to set the reader chunk size to automatically get one partition per GPU
chunksize = dask_cugraph.get_chunksize(input_data_path)

# Multi-GPU CSV reader
e_list = dask_cudf.read_csv(input_data_path,
        chunksize = chunksize,
        delimiter=' ',
        names=['src', 'dst'],
        dtype=['int32', 'int32'])

G = cugraph.DiGraph()
G.from_dask_cudf_edgelist(e_list, source='src', destination='dst')

# now run PageRank
pr_df = dask_cugraph.pagerank(G, tol=1e-4)

# All done, clean up