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

2025/11/13 by Ruihua He, Agarwal, Aarush, J. Kieseler +6
Computer Science · #Advanced Graph Neural Networks #Cloud Computing and Resource Management #Distributed #FOS: Computer and information sciences #Graph Theory and Algorithms #Parallel #and Cluster Computing (cs.DC)

paper · pdf · doi:10.48550/arxiv.2511.10442

openalex publication_date 2025/11/13 · openalex created_date 2025/11/15 · openalex updated_date 2026/07/28

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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