基于SIMD指令的决策树评价优化
Optimization of Decision Tree Evaluation Using SIMD Instructions
摘要：
决策森林（决策树集成）是最流行的机器学习算法之一。为了在大数据上使用大型模型，例如使用学习排名模型的文档评分，我们需要有效地评估这些模型。在本文中，我们探讨了流行的CatBoost库的祖先MatrixNet。这两个库都使用SSE指令集对CPU进行评分。本文研究了AVX指令集提供的更有效地评估模型的机会。我们在二进制化阶段（节点条件比较）实现了35%的加速，在排名模型的树应用阶段实现了20%的加速。
英文摘要：
Decision forest (decision tree ensemble) is one of the most popular machine learning algorithms. To use large models on big data, like document scoring with learning-to-rank models, we need to evaluate these models efficiently. In this paper, we explore MatrixNet, the ancestor of the popular CatBoost library. Both libraries use the SSE instruction set for scoring on CPU. This paper investigates the opportunities given by the AVX instruction set to evaluate models more efficiently. We achieved 35% speedup on the binarization stage (nodes conditions comparison), and 20% speedup on the trees apply stage on the ranking model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
