GPU accelerated t-distributed stochastic neighbor embedding
Modern datasets and models are notoriously difficult to explore and analyze due to their
inherent high dimensionality and massive numbers of samples. Existing visualization
methods which employ dimensionality reduction to two or three dimensions are often
inefficient and/or ineffective for these datasets. This paper introduces t-SNE-CUDA, a GPU-
accelerated implementation of t-Distributed Symmetric Neighbor Embedding (t-SNE) for
visualizing datasets and models. t-SNE-CUDA significantly outperforms current …
inherent high dimensionality and massive numbers of samples. Existing visualization
methods which employ dimensionality reduction to two or three dimensions are often
inefficient and/or ineffective for these datasets. This paper introduces t-SNE-CUDA, a GPU-
accelerated implementation of t-Distributed Symmetric Neighbor Embedding (t-SNE) for
visualizing datasets and models. t-SNE-CUDA significantly outperforms current …
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