- By:
- Sao, Piyush K; Prokopenko, Andrey V; Lebrun-Grandie, Damien T
- Page Number:
- 908-918
- Book Title:
- ICPP '24: Proceedings of the 53rd International Conference on Parallel Processing
- Publication Date:
- March 12, 2026
- Publisher Location:
- Association for Computing Machinery, New York, New York, United States of America
- Conference Name:
- 53rd International Conference on Parallel Processing (ICPP2024)
- Conference Location:
- Visby, Gotland, Sweden
- Conference Sponsor:
- ACM
- View DOI Listing:
- https://doi.org/10.1145/3673038.3673148
Abstract
This paper introduces Pandora, a parallel algorithm for computing dendrograms, the hierarchical cluster trees for single linkage clustering (SLC). Current parallel approaches construct dendrograms by partitioning a minimum spanning tree and removing edges. However, they struggle with skewed, hard-to-parallelize real-world dendrograms. Consequently, computing dendrograms is the sequential bottleneck in HDBSCAN*[21], a popular SLC variant. Pandora uses recursive tree contraction to address this limitation. Pandora contracts nodes to construct progressively smaller trees. It computes the smallest contracted dendrogram and expands it by inserting contracted edges. This recursive strategy is highly parallel, skew-independent, work-optimal, and well-suited for GPUs and multicores. We develop a performance portable implementation of Pandora in Kokkos[31] and evaluate its performance on multicore CPUs and multi-vendor GPUs (e.g., Nvidia, AMD) for dendrogram construction in HDBSCAN*. Multithreaded Pandora is 2.2x faster than the current best-multithreaded implementation. Our GPU version achieves 6-20x speedup on AMD GPUs and 10-37x on NVIDIA GPUs over multithreaded Pandora. Pandora removes HDBSCAN*’s sequential bottleneck, greatly boosting efficiency, particularly with GPUs.
