FastGraph: Optimized GPU-Enabled Algorithms for Fast Graph Building and Message Passing
Aarush Agarwal, Raymond He, Jan Kieseler, Matteo Cremonesi, and Shah Rukh Qasim

TL;DR
FastGraph is a GPU-optimized algorithm that significantly accelerates graph construction and message passing in low-dimensional spaces, enabling faster GNN workflows with minimal memory overhead.
Contribution
It introduces a novel GPU-resident, bin-partitioned k-nearest neighbor algorithm with adaptive tuning, outperforming existing libraries in speed and efficiency.
Findings
Achieves 20-40x speedup over FAISS, ANNOY, SCANN
Operates efficiently in dimensions less than 10
Enhances GNN performance in applications like physics and image analysis
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGraph Theory and Algorithms · Advanced Graph Neural Networks · Cloud Computing and Resource Management
