Improving Barnes-Hut t-SNE Algorithm in Modern GPU Architectures with Random Forest KNN and Simulated Wide-Warp

The t-Distributed Stochastic Neighbor Embedding (t-SNE) is a widely used technique for dimensionality reduction but is limited by its scalability when applied to large datasets. Recently, BH-tSNE was proposed; this is a successful approximation that transforms a step of the original algorithm into an N-Body simulation problem that can be solved by a modified Barnes-Hut algorithm. However, this improvement still has limitations to process large data volumes (millions of records). Late studies, such as t-SNE-CUDA, have used GPUs to implement highly parallel BH-tSNE. In this research we have developed a new GPU BH-tSNE implementation that produces the embedding of multidimensional data points into three-dimensional space. We examine scalability issues in two of the most expensive steps of GPU BH-tSNE by using efficient memory access strategies , recent acceleration techniques , and a new approach to compute the KNN graph structure used in BH-tSNE with GPU. Our design allows up to 460% faster execution when compared to the t-SNE-CUDA implementation. Although our SIMD acceleration techniques were used in a modern GPU setup, we have also verified a potential for applications in the context of multi-core processors.