FastGraph: Optimized GPU-Enabled Algorithms for Fast Graph Building and Message Passing
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912707205136384 |
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| author | Agarwal, Aarush He, Raymond Kieseler, Jan Cremonesi, Matteo Qasim, Shah Rukh |
| author_facet | Agarwal, Aarush He, Raymond Kieseler, Jan Cremonesi, Matteo Qasim, Shah Rukh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_10442 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FastGraph: Optimized GPU-Enabled Algorithms for Fast Graph Building and Message Passing Agarwal, Aarush He, Raymond Kieseler, Jan Cremonesi, Matteo Qasim, Shah Rukh Distributed, Parallel, and Cluster Computing 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. |
| title | FastGraph: Optimized GPU-Enabled Algorithms for Fast Graph Building and Message Passing |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2511.10442 |