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arXiv · 2511.10442

FastGraph: Optimized GPU-Enabled Algorithms for Fast Graph Building and Message Passing

Aarush Agarwal·Raymond He·Jan Kieseler·Matteo Cremonesi·Shah Rukh Qasim
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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.

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Aarush Agarwal, Raymond He, Jan Kieseler, Matteo Cremonesi, Shah Rukh Qasim. 2025-11-13. FastGraph: Optimized GPU-Enabled Algorithms for Fast Graph Building and Message Passing. https://arxiv.org/abs/2511.10442

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