GraphGeeks In Discussion: RAPIDS and cuGraph with NVIDIA's Joe Eaton

This podcast episode features host Amy Hodler in conversation with Joe Eaton, NVIDIA Distinguished System Engineer, discussing graph analytics acceleration technologies. This discussion covered: - How RAPIDS and cuGraph are changing graph analytics through GPU acceleration - Their innovative approach to scaling NetworkX on GPUs without requiring code modifications - An exploration of GNNs, graph embeddings, and vector search applications We also looked at current trends in graph technology and their real-world implications. Joe provided pointers to resources (below) for people just getting started as well as his picks for exciting graph-related talk at the upcoming NVIDIA GTC conference. Getting Started with RAPIDS and cuGraph https://rapids.ai/ https://github.com/rapidsai https://rapids.ai/nx-cugraph/ Graph Talks at GTC https://nvda.ws/4bofZ4e RAPIDS in 2025: Accelerated Data Science Everywhere [S73290]: https://www.nvidia.com/gtc/session-catalog/?regcode=ref-inor-862057&ncid=ref-inor-862057&tab.catalogallsessionstab=16566177511100015Kus&search=S73290#/session/1728323939538001F71p HybridRAG: Make RAG More Accurate by Combining Graph and Vector-Based Retrieval [CWE72566]: https://www.nvidia.com/gtc/session-catalog/?regcode=ref-inor-862057&ncid=ref-inor-862057&tab.catalogallsessionstab=16566177511100015Kus&search=CWE72566#/session/1727449059487001PWqT Building Semantic Search and Data Mining Applications With GPU-Accelerated Vector Search [CWE71289]: https://www.nvidia.com/gtc/session-catalog/?regcode=ref-inor-862057&ncid=ref-inor-862057&tab.catalogallsessionstab=16566177511100015Kus&search=CWE71289#/session/1725505570355001Yi6d