A single-tree algorithm to compute the Euclidean minimum spanning tree on GPUs

Conference · Sun Jan 01 04:00:00 EST 2023 · Proceedings of the International Conference on Parallel Processing

DOI:https://doi.org/10.1145/3545008.3546185· OSTI ID:1922321

Prokopenko, Andrey ^[1]; Sao, Piyush ^[1]; Lebrun-Grandie, Damien ^[1]

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1. ORNL

Computing the Euclidean minimum spanning tree (Emst) is a computationally demanding step of many algorithms. While work-efficient serial and multithreaded algorithms for computing Emst are known, designing an efficient GPU algorithm is challenging due to a complex branching structure, data dependencies, and load imbalances. In this paper, we propose a single-tree $$Bor\mathring{u}vka$$-based algorithm for computing Emst on GPUs. We use an efficient nearest neighbor algorithm and reduce the number of the required distance calculations by avoiding traversing subtrees with leaf nodes in the same component. The developed algorithms are implemented in a performance portable way using ArborX, an open-source geometric search library based on the Kokkos framework. We evaluate the proposed algorithm on various 2D and 3D datasets, show and compare it with the current state-of-the-art open-source CPU implementations. We demonstrate 4-24 × speedup over the fastest multi-threaded implementation. We prove the portability of our implementation by providing results on a variety of hardware: AMD EPYC 7763, Nvidia A100 and AMD MI250X. We show scalability of the implementation, computing Emst for 37 million 3D cosmological dataset in under a 0.5 second on a single A100 Nvidia GPU.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE; USDOE Office of Science (SC)
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1922321
• Conference Information: Journal Name: Proceedings of the International Conference on Parallel Processing Journal Volume: 2022
• Country of Publication: United States
• Language: English