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t-SNE remains one of the most popular embedding techniques for visualizinghigh-dimensional data. Most standard packages of t-SNE, such as scikit-learn,use the Barnes-Hut t-SNE (BH t-SNE) algorithm for large datasets. However,existing CPU implementations of this algorithm are inefficient. In this work,we accelerate the BH t-SNE on CPUs via cache optimizations, SIMD, parallelizingsequential steps, and improving parallelization of multithreaded steps. Ourimplementation (Acc-t-SNE) is up to 261x and 4x faster than scikit-learn andthe state-of-the-art BH t-SNE implementation from daal4py, respectively, on a32-core Intel(R) Icelake cloud instance.

Accelerating Barnes-Hut t-SNE Algorithm by Efficient Parallelization on Multi-Core CPUs