Description
Graph analytics can be accelerated on NVIDIA GPUs through the RAPIDS cuGraph library. This is useful for data-science and machine-learning workloads involving large graphs, PageRank, traversal, or connectivity analysis.
It depends on a compatible CUDA and RAPIDS stack. Users need matching GPU drivers, Python or C++ integration, and enough GPU memory for the graph data.