Improve the speed of t-sne implementation in python for huge data I would like to do dimensionality reduction on nearly 1 million vectors each with 200 dimensions(doc2vec). 
I am using TSNE implementation from sklearn.manifold module for it and the major problem is time complexity. Even with method = barnes_hut, the speed of computation is still low. Some time even it runs out of Memory.

I am running it on a 48 core processor with 130G RAM. Is there a method to run it parallely or make use of the plentiful resource to speed up the process.
You must look at this Multicore implementation of t-SNE.

I actually tried it and can vouch for its superior performance.