论文标题
对CPU的遗忘决策树的合奏评估的优化
Optimization of Oblivious Decision Tree Ensembles Evaluation for CPU
论文作者
论文摘要
Catboost是一个受欢迎的机器学习库。 Catboost模型基于遗忘的决策树，使培训和评估迅速。 Catboost有许多应用程序，有些需要低延迟和高吞吐量评估。本文研究了在单核CPU计算中提高CATBOOST性能的可能性。我们探索AVX指令集提供的新功能，以优化评估。我们使用AVX2说明将性能提高了20-40％，而没有质量影响。我们还引入了速度和质量之间的新权衡。使用Float16进行叶子值和AVX-512说明，我们实现了50-70％的加速。
CatBoost is a popular machine learning library. CatBoost models are based on oblivious decision trees, making training and evaluation rapid. CatBoost has many applications, and some require low latency and high throughput evaluation. This paper investigates the possibilities for improving CatBoost's performance in single-core CPU computations. We explore the new features provided by the AVX instruction sets to optimize evaluation. We increase performance by 20-40% using AVX2 instructions without quality impact. We also introduce a new trade-off between speed and quality. Using float16 for leaf values and AVX-512 instructions, we achieve 50-70% speed-up.
