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. 
Improve the speed of t-sne implementation in python for huge data
python;bigdata;nlp;scikit learn;dimensionality reduction
Since, there are no answers in SO, I have asked myself in github page and the issue has been closed by stating the following reply by GaelVaroquaux..If you only want to parallelise vector operation, then you should use a  build of numpy compiled with MKL (don't attempt to do it yourself, it's  challenging).There could be approaches to high-level parallelism in the algorithm  itself, which would probably lead to larger gains. However, after a quick  look at the code, I didn't see any clear way of doing that.I am going to ahead and close this issue, as it is more of a blue-sky  whish list. I completely agree, I would like TSNE to go faster, and it  would be great is parallelism was easy. But in the current state of  affairs, more work is required to be in a state where we can tackle such  wish list.