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.

中文翻译：

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使用随机森林 KNN 和模拟 Wide-Warp 改进现代 GPU 架构中的 Barnes-Hut t-SNE 算法

t 分布随机邻域嵌入 (t-SNE) 是一种广泛使用的降维技术，但在应用于大型数据集时受到其可扩展性的限制。最近提出了BH-tSNE；这是一个成功的近似，它将原始算法的一个步骤转换为一个 N-Body 模拟问题，可以通过修改后的 Barnes-Hut 算法来解决。但是，这种改进在处理大量数据（数百万条记录）时仍然存在局限性。后期的研究，例如 t-SNE-CUDA，已经使用 GPU 来实现高度并行的 BH-tSNE。在这项研究中，我们开发了一种新的 GPU BH-tSNE 实现，它可以将多维数据点嵌入到三维空间中。我们通过使用高效的内存访问策略，在 GPU BH-tSNE 的两个最昂贵的步骤中检查可扩展性问题，最近的加速技术，以及一种使用 GPU 计算 BH-tSNE 中使用的 KNN 图结构的新方法。与 t-SNE-CUDA 实施相比，我们的设计可将执行速度提高 460%。尽管我们的 SIMD 加速技术已用于现代 GPU 设置，但我们也验证了在多核处理器环境中的应用潜力。