{"ID":2842350,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.10442","arxiv_id":"2511.10442","title":"FastGraph: Optimized GPU-Enabled Algorithms for Fast Graph Building and Message Passing","abstract":"We introduce FastGraph, a novel GPU-optimized k-nearest neighbor algorithm specifically designed to accelerate graph construction in low-dimensional spaces (2-10 dimensions), critical for high-performance graph neural networks. Our method employs a GPU-resident, bin-partitioned approach with full gradient-flow support and adaptive parameter tuning, significantly enhancing both computational and memory efficiency. Benchmarking demonstrates that FastGraph achieves a 20-40x speedup over state-of-the-art libraries such as FAISS, ANNOY, and SCANN in dimensions less than 10 with virtually no memory overhead. These improvements directly translate into substantial performance gains for GNN-based workflows, particularly benefiting computationally intensive applications in low dimensions such as particle clustering in high-energy physics, visual object tracking, and graph clustering.","short_abstract":"We introduce FastGraph, a novel GPU-optimized k-nearest neighbor algorithm specifically designed to accelerate graph construction in low-dimensional spaces (2-10 dimensions), critical for high-performance graph neural networks. Our method employs a GPU-resident, bin-partitioned approach with full gradient-flow support...","url_abs":"https://arxiv.org/abs/2511.10442","url_pdf":"https://arxiv.org/pdf/2511.10442v1","authors":"[\"Aarush Agarwal\",\"Raymond He\",\"Jan Kieseler\",\"Matteo Cremonesi\",\"Shah Rukh Qasim\"]","published":"2025-11-13T16:06:13Z","proceeding":"cs.DC","tasks":"[\"cs.DC\"]","methods":"[\"Graph Neural Network\"]","has_code":false}