Asynchronous CPU-GPU Density Peak Clustering via Granular-Ball Representation

Density Peak Clustering (DPC) relies on extensive distance computations, leading to high time and memory costs when applied to large-scale data. Although existing granular-ball methods reduce the computational scale through coarse-grained representation, their scalability is still limited by the quality of granular-ball construction and the sequential execution of subsequent computations. To address these issues, we propose an incremental CPU-GPU parallel density peak clustering method based on granular-ball representation, termed ICG-DPC. First, the data space is partitioned into local regions, where granular balls are adaptively refined according to local density and structural dispersion. The balls are further split along the principal direction of the data distribution to better preserve complex spatial structures, thereby compressing the original data into a much smaller set of granular balls. Then, an asynchronous CPU-GPU collaborative framework is developed, in which the CPU dynamically performs granular-ball refinement and structural maintenance, while the GPU parallelizes local neighborhood construction, density estimation, and density-peak computation. A version-consistency mechanism is further employed to coordinate dynamic structural updates with parallel computation. Experimental results on large-scale synthetic and real-world datasets demonstrate that ICG-DPC maintains competitive clustering performance while providing favorable computational efficiency and scalability.