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arXiv Machine Learning By Diego Sanches Nere dos Santos, Luiz Carlos Bambirra Torres

GPU-Accelerated Construction of the Gabriel Graph: Parallelization Strategies and Applications to Large-Margin Classification and Graph-Based Clustering

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The paper introduces GPU-based parallelization techniques for constructing the Gabriel graph, a proximity graph used in computational geometry and machine learning. By mapping thread indices to vertex pairs, employing a bitmask-based edge-list construction, and using thread coarsening, the authors achieve up to 8484× speedups over a CPU baseline without sacrificing accuracy. The accelerated graph enables efficient large‑margin classification and a new density‑aware clustering method that competes with DBSCAN while eliminating the need for a density radius parameter.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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