Achieve up to 500x Faster NetworkX with Zero Code Changes Using NVIDIA cuGraph

NetworkX, a popular Python graph analytics library, now includes an in-memory GPU backend powered by cuGraph for popular graph algorithms such as Louvain, betweenness centrality, PageRank, and others.
NVIDIA cuGraph creates speedups of 50x to 500x on large graph workloads (100K+ nodes and 1M+ edges) when compared with CPU-only NetworkX.
Widely known use cases that involve graph analytics include social networks, fraud detection, recommendation systems, supply chain management, pharmaceutical drug development, and others.
📝Get started in cuGraph documentation: docs.rapids.ai...
📝Technical Blog “NetworkX Introduces Zero Code Change Acceleration Using NVIDIA cuGraph” to learn more: nvda.ws/4hu9ijZ
📗 Try the example Colab notebook: nvda.ws/4drM4re
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NetworkX, Graph Analytics, Large Graphs, GNNs, RAPIDS, NVIDIA, Data Processing