mlx-vis: GPU-Native Dimensionality Reduction on Apple Silicon
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Abstract (arXiv)
Dimensionality reduction is a foundational tool for visualizing high-dimensional data, yet its reference implementations span a fragmented stack of CPU-bound Python packages that leaves the Metal GPU on Apple Silicon entirely unused. We present mlx-vis, a library that reimplements seven widely used dimensionality reduction methods and k-nearest neighbor graph construction in pure MLX, with every stage -- from PCA preprocessing through embedding optimization to a circle-splatting renderer -- executing on GPU. On Fashion-MNIST 70K, all seven methods embed in 2.1--4.6 s on an M3 Ultra, achieving 3--13x speedups over CPU baselines while reducing the entire dependency stack to MLX and NumPy. The same pipeline scales to ten million points on a single workstation. Code at https://github.com/hanxiao/mlx-vis
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 18 references that are in the map. First posted on arXiv 4 Mar 2026.
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References · 8
- 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
- 2020Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualizationcited by 170
- 2017Efficient Algorithms for t-distributed Stochastic Neighborhood Embeddingcited by 75
- 2019TriMap: Large-scale Dimensionality Reduction Using Tripletscited by 59
- 2022From t-SNE to UMAP with contrastive learningcited by 28
- 2024Dimension Reduction with Locally Adjusted Graphscited by 9
- 2025DREAMS: Preserving both Local and Global Structure in Dimensionality Reductioncited by 4
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