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CUGRAPH
GPU-accelerated graph analytics with a NetworkX-compatible API
Apache-2.0
ABOUT
Graph analytics workloads on real-world graphs — social networks, knowledge graphs, fraud networks, and infrastructure topologies — are computationally intensive and quickly exceed the capacity of CPU-based libraries like NetworkX. cuGraph solves this by reimplementing graph algorithms (PageRank, Louvain community detection, betweenness centrality, BFS, and SSSP) on GPU hardware, enabling analysts and data scientists to run graph computations on billion-edge graphs in seconds instead of hours without changing their high-level API patterns.
INSTALL
pip install cugraph-cu12INTEGRATION GUIDE
1. Run PageRank, centrality, and community detection on billion-edge knowledge graphs at GPU speeds
2. Detect fraud rings and anomalous connection patterns in large-scale transaction networks in real time
3. Analyze social network graphs and recommendation system graphs with GPU-accelerated pathfinding
4. Perform large-scale graph clustering and partitioning for distributed ML training and data pipelines
5. Compute shortest paths and connectivity in infrastructure and logistics networks at interactive speeds
TAGS
gpugraph-analyticsnetworkxrapidsgraphnvidiaparallelgraph-algorithms