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- W4327989978 abstract "General Purpose computing on Graphical Processing Units (GPGPU) has resulted in unprecedented levels of speedup over its CPU counterparts, allowing programmers to harness the computational power of GPU shader cores to accelerate other computing applications. But this style of acceleration is best suited for regular computations (e.g., linear algebra). Recent GPUs feature new Ray Tracing (RT) cores that instead speed up the irregular process of ray tracing using Bounding Volume Hierarchies. While these cores seem limited in functionality, they can be used to accelerate n-body problems by leveraging RT cores to accelerate the required distance computations. In this work, we propose RT-DBSCAN, the first RT-accelerated DBSCAN implementation. We use RT cores to accelerate Density-Based Clustering of Applications with Noise (DBSCAN) by translating fixed-radius nearest neighbor queries to ray tracing queries. We show that leveraging the RT hardware results in speedups between 1.3x to 4x over current state-of-the-art, GPU-based DBSCAN implementations." @default.
- W4327989978 created "2023-03-21" @default.
- W4327989978 creator A5059294736 @default.
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- W4327989978 date "2023-03-16" @default.
- W4327989978 modified "2023-09-27" @default.
- W4327989978 title "RT-DBSCAN: Accelerating DBSCAN using Ray Tracing Hardware" @default.
- W4327989978 doi "https://doi.org/10.48550/arxiv.2303.09655" @default.
- W4327989978 hasPublicationYear "2023" @default.
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