GPU加速的BART模型，速度提升200倍，让统计回归更高效
Very fast Bayesian Additive Regression Trees on GPU
- 利用GPU并行计算优化贝叶斯加性回归树算法
- 在百万级数据上运行速度比单核CPU快200倍
- 适合需要高精度且不希望手动调参的数据分析场景
贝叶斯加性回归树（BART）是一种基于决策树集成的非参数贝叶斯回归方法，统计性能通常优于其他通用方法，且无需大量调参，是许多统计学家的首选工具。然而，由于计算耗时较长，当样本量超过1万至10万时便难以应用，使其相比XGBoost等竞争方法处于边缘地位。本文提出一种支持GPU的BART实现，相较于单核CPU，速度最快可提升200倍，使BART在运行效率上与XGBoost相当。该实现已开源为Python包bartz。
原文摘要 · Abstract (English)
Bayesian Additive Regression Trees (BART) is a nonparametric Bayesian regression technique based on an ensemble of decision trees. It is part of the toolbox of many statisticians. The overall statistical quality of the regression is typically higher than other generic alternatives, and it requires less manual tuning, making it a good default choice. However, it is a niche method compared to its natural competitor XGBoost, due to the longer running time, making sample sizes above 10,000-100,000 a nuisance. I present a GPU-enabled implementation of BART, faster by up to 200x relative to a single CPU core, making BART competitive in running time with XGBoost. This implementation is available in the Python package bartz.
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