Improving Barnes-Hut t-SNE Scalability in GPU with Efficient Memory Access Strategies

The t-Distributed Stochastic Neighbor Embedding (t-SNE) is a widely used technique for dimensionality reduction, however, its application to large datasets is still an issue. In this sense, BH-tSNE was proposed, which is a successful approximation where the Barnes-Hut algorithm is used instead of computing a step of the t-SNE with quadratic computational time complexity. Even so, 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, a new GPU BH-tSNE implementation is proposed using efficient memory access strategies and recent acceleration techniques. Moreover, the embedding of multidimensional data points into three-dimensional space is applied. We examine scalability issues in one of the most expensive steps of GPU BH-tSNE. Our design allows up to 340% faster execution when compared to the t-SNE-CUDA implementation.