A developer has created the first known GPU-accelerated implementation of Forman-Ricci curvature-based graph clustering in CUDA, targeting community detection in large networks. The algorithm uses Ricci flow to differentiate intra-cluster edges from bridges, then applies a modularity-based threshold to reveal structure. It was built on an RTX 4090 and compared against CPU benchmarks.

The project emerged from a desire to move beyond re-implementing existing code. Forman-Ricci was chosen over Ollivier-Ricci for its relative simplicity and lack of prior GPU implementations. Performance tests on a Stochastic Block Model showed dramatic speedups, processing 100,000 nodes with 102 million edges in 625 seconds on the GPU versus a CPU time deemed too high to complete.

Debugging involved using Claude AI, which introduced its own bugs, including incorrect equation substitutions and overfitting to ground truth data. The developer provided detailed NCU profiling reports, showing kernels utilizing over 80% of available compute or memory performance. The code is available on GitHub, seeking feedback for optimization and career development in machine learning.