[2603.04035] mlx-vis: GPU-Accelerated Dimensionality Reduction and Visualization on Apple Silicon

Computer Science > Machine Learning arXiv:2603.04035 (cs) [Submitted on 4 Mar 2026] Title:mlx-vis: GPU-Accelerated Dimensionality Reduction and Visualization on Apple Silicon Authors:Han Xiao View a PDF of the paper titled mlx-vis: GPU-Accelerated Dimensionality Reduction and Visualization on Apple Silicon, by Han Xiao View PDF HTML (experimental) Abstract:mlx-vis is a Python library that implements six dimensionality reduction methods and a k-nearest neighbor graph algorithm entirely in MLX, Apple's array framework for Apple Silicon. The library provides UMAP, t-SNE, PaCMAP, TriMap, DREAMS, CNE, and NNDescent, all executing on Metal GPU through a unified fit_transform interface. Beyond embedding computation, mlx-vis includes a GPU-accelerated circle-splatting renderer that produces scatter plots and smooth animations without matplotlib, composing frames via scatter-add alpha blending on GPU and piping them to hardware H.264 encoding. On Fashion-MNIST with 70,000 points, all methods complete embedding in 2.1-3.8 seconds and render 800-frame animations in 1.4 seconds on an M3 Ultra, with the full pipeline from raw data to rendered video finishing in 3.6-5.2 seconds. The library depends only on MLX and NumPy, is released under the Apache 2.0 license, and is available at this https URL. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2603.04035 [cs.LG]   (or arXiv:2603.04035v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2603.04035 Focus to learn more arXiv-is...