DmitryUlyanov/Multicore-TSNE

Health v2 · maintenance only

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1909 stars · 229 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A multicore modification of Barnes-Hut t-SNE written in C++ with Python (CFFI) and Torch wrappers for fast dimensionality reduction of high-dimensional data. It is faster than sklearn's t-SNE even on a single core and scales with available CPU cores.

Use cases

• visualize high-dimensional datasets like MNIST in 2D with t-SNE
• speed up t-SNE embedding of large datasets using multiple cores
• run t-SNE on a 300k x 3000 feature table quickly
• replace sklearn TSNE with a faster Barnes-Hut implementation
• embed features for a Kaggle competition under a deadline
• compute t-SNE embeddings from Torch tensors

When to choose

• you need t-SNE on large high-dimensional data and sklearn is too slow
• you have many CPU cores and want parallel nearest-neighbor probability computation
• you want a Python or Torch wrapper around a fast C++ Barnes-Hut t-SNE

When to avoid

• your data has very low dimensionality, where parallelization gains are minimal
• you need actively maintained software with recent development
• you need UMAP or other scalable dimensionality reduction alternatives instead of t-SNE

Facets

library · maturity maintenance

machine-learning data-visualization math machine-learning data-visualization data-science python cpp cross-platform tsne barnes-hut dimensionality-reduction multicore parallel-computing embedding

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