Accelerating FastDEC Clustering Algorithm with GPUs
Mi Li, Eibe Frank, Bernhard Pfahringer
Abstract
Fast Dominance Estimation Clustering (FastDEC) is an efficient variant of Density Peak Clustering (DPC) that achieves high clustering quality through dominance-based density estimation, but remains constrained by the computational cost of exact k-nearest-neighbor (k-NN) construction and dominance component (DC) processing. We present SymWarp-FastDEC, a GPU-accelerated implementation of FastDEC. At its core is SymTrikNN, an exact self-k-NN kernel that exploits search symmetry using upper-triangular traversal and warp-cooperative top-k selection. We also introduce WP-DC, a warp-parallel DC reduction algorithm designed for modern GPUs. Combined with memory-adaptive execution planning and stream scheduling, WP-DC eliminates DC reduction as a runtime bottleneck. Experiments on synthetic and real-world datasets show that SymWarp-FastDEC produces clustering outputs identical to the original FastDEC on all evaluated equivalence benchmarks. Compared with a FastDEC variant using FAISS GpuIndexFlatL2 for exact k-NN search, our approach achieves approximately 1.7x to 4.9x higher end-to-end performance, with the advantage generally increasing with dimensionality. Compared with the original single-threaded Python FastDEC implementation, speedups reach approximately 2.5 x 10^4 in a very high-dimensional case where its k-d-tree-based search degrades; this extreme result is not representative of typical acceleration.