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GPUMAP: GPU-Accelerated Progressive Uniform Manifold Approximation and Projection
arXiv cs.HC0 views
arXiv:2611.06191v1 Announce Type: cross Abstract: Dimensionality reduction (DR) techniques are fundamental to the analysis of high-dimensional data, yet their substantial computational cost limits their use in interactive settings. To address this limitation, we present GPU-accelerated Progressive Uniform Manifold Approximation and Projection (GPUMAP), an efficient progressive DR algorithm designed for interactive use. We identify three atomic operations within UMAP and restructure them into a progressive pipeline. As part of this adaptation, we introduce modifications to UMAP, including operation queues, instance-wise local join, and scheduling strategies. We further accelerate computation by leveraging GPU parallelism, enabling interactive analysis of million-scale datasets on commodity GPU hardware. Finally, we employ our implementation to systematically study the behavior of progressive DR, investigating how projections evolve over time relative to the ground-truth projection and how biased data streams influence the quality and stability of the resulting projections.
Source：https://arxiv.org/abs/2611.06191PreviousFireOPS: A Human-Centric Emergency Operations PlatformNextA Cracked Mirror: How People Perceive Gender Performance by Large Language Models
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