Skip to content


Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?


Failed to load latest commit information.
Latest commit message
Commit time
October 29, 2018 00:39
October 20, 2017 13:13
October 23, 2017 16:08
November 9, 2017 15:01
October 19, 2016 08:47
February 10, 2020 15:12

Multicore t-SNE Build Status

This is a multicore modification of Barnes-Hut t-SNE by L. Van der Maaten with python and Torch CFFI-based wrappers. This code also works faster than sklearn.TSNE on 1 core.

What to expect

Barnes-Hut t-SNE is done in two steps.

  • First step: an efficient data structure for nearest neighbours search is built and used to compute probabilities. This can be done in parallel for each point in the dataset, this is why we can expect a good speed-up by using more cores.

  • Second step: the embedding is optimized using gradient descent. This part is essentially consecutive so we can only optimize within iteration. In fact some parts can be parallelized effectively, but not all of them a parallelized for now. That is why second step speed-up will not be that significant as first step sepeed-up but there is still room for improvement.

So when can you benefit from parallelization? It is almost true, that the second step computation time is constant of D and depends mostly on N. The first part's time depends on D a lot, so for small D time(Step 1) << time(Step 2), for large D time(Step 1) >> time(Step 2). As we are only good at parallelizing step 1 we will benefit most when D is large enough (MNIST's D = 784 is large, D = 10 even for N=1000000 is not so much). I wrote multicore modification originally for Springleaf competition, where my data table was about 300000 x 3000 and only several days left till the end of the competition so any speed-up was handy.


1 core

Interestingly, that this code beats other implementations. We compare to sklearn (Barnes-Hut of course), L. Van der Maaten's bhtsne, py_bh_tsne repo (cython wrapper for bhtsne with QuadTree). perplexity = 30, theta=0.5 for every run. In fact py_bh_tsne repo works at the same speed as this code when using more optimization flags for compiler.

This is a benchmark for 70000x784 MNIST data:

Method Step 1 (sec) Step 2 (sec)
MulticoreTSNE(n_jobs=1) 912 350
bhtsne 4257 1233
py_bh_tsne 1232 367
sklearn(0.18) ~5400 ~20920

I did my best to find what is wrong with sklearn numbers, but it is the best benchmark I could do (you can find test script in python/tests folder).


This table shows a relative to 1 core speed-up when using n cores.

n_jobs Step 1 Step 2
1 1x 1x
2 1.54x 1.05x
4 2.6x 1.2x
8 5.6x 1.65x

How to use

Python and torch wrappers are available.



Directly from pypi

pip install MulticoreTSNE

From source

Make sure cmake is installed on your system, and you will also need a sensible C++ compiler, such as gcc or llvm-clang. On macOS, you can get both via homebrew.

To install the package, please do:

git clone
cd Multicore-TSNE/
pip install .

Tested with both Python 2.7 and 3.6 (conda) and Ubuntu 14.04.


You can use it as a near drop-in replacement for sklearn.manifold.TSNE.

from MulticoreTSNE import MulticoreTSNE as TSNE

tsne = TSNE(n_jobs=4)
Y = tsne.fit_transform(X)

Please refer to sklearn TSNE manual for parameters explanation.

This implementation n_components=2, which is the most common case (use Barnes-Hut t-SNE or sklearn otherwise). Also note that some parameters are there just for the sake of compatibility with sklearn and are otherwise ignored. See MulticoreTSNE class docstring for more info.

MNIST example

from sklearn.datasets import load_digits
from MulticoreTSNE import MulticoreTSNE as TSNE
from matplotlib import pyplot as plt

digits = load_digits()
embeddings = TSNE(n_jobs=4).fit_transform(
vis_x = embeddings[:, 0]
vis_y = embeddings[:, 1]
plt.scatter(vis_x, vis_y,,"jet", 10), marker='.')
plt.clim(-0.5, 9.5)


You can test it on MNIST dataset with the following command:

python MulticoreTSNE/examples/ <n_jobs>

Note on jupyter use

To make the computation log visible in jupyter please install wurlitzer (pip install wurlitzer) and execute this line in any cell beforehand:

%load_ext wurlitzer

Memory leakages are possible if you interrupt the process. Should be OK if you let it run until the end.


To install execute the following command from repository folder:

luarocks make torch/tsne-1.0-0.rockspec


luarocks install

You can run t-SNE like that:

tsne = require 'tsne'

Y = tsne(X, n_components, perplexity, n_iter, angle, n_jobs)

torch.DoubleTensor type only supported for now.


Inherited from original repo's license.

Future work

  • Allow other types than double
  • Improve step 2 performance (possible)


Please cite this repository if it was useful for your research:

  author = {Ulyanov, Dmitry},
  title = {Multicore-TSNE},
  year = {2016},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{}},

Of course, do not forget to cite L. Van der Maaten's paper