Optimization of Decision Tree Evaluation Using SIMD Instructions

05/15/2022

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by   Alexey Mironov, et al.

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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 (nodes conditions comparison), and 20 ranking model.