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This repo is an optimized CUDA version of FIt-SNE algorithm with associated python modules. We find that our implementation of t-SNE can be up to 1200x faster than Sklearn, or up to 50x faster than Multicore-TSNE when used with the right GPU. You can install binaries with anaconda for CUDA version 10.1 and 10.2 using conda install tsnecuda -c conda-forge. Tsnecuda supports CUDA versions 9.0 and later through source installation, check out the wiki for up to date installation instructions. Time taken compared to other state of the art algorithms on synthetic datasets with 50 dimensions and four clusters for varying numbers of points. Note the log scale on both the points and time axis, and that the scale of the x-axis is in thousands of points (thus, the values on the x-axis range from 1K to 10M points. Dashed lines on SkLearn, BH-TSNE, and MULTICORE-4 represent projected times. Projected scaling assumes an O(nlog(n)) implementation.
Features
- To install our library, follow the installation instructions
- Like many of the libraries available, the python wrappers subscribe to the same API as sklearn.manifold.TSNE.
- We only support n_components=2
- Our code is built using components from FAISS, the Lonestar GPU library
- You can install binaries with anaconda for CUDA version 10.1 and 10.2
- This repo is an optimized CUDA version of FIt-SNE algorithm
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