  Gradient boosting decision tree (GBDT) is a widely-used machine learning
algorithm in both data analytic competitions and real-world industrial
applications. Further, driven by the rapid increase in data volume, efforts
have been made to train GBDT in a distributed setting to support large-scale
workloads. However, we find it surprising that the existing systems manage the
training dataset in different ways, but none of them have studied the impact of
data management. To that end, this paper aims to study the pros and cons of
different data management methods regarding the performance of distributed
GBDT. We first introduce a quadrant categorization of data management policies
based on data partitioning and data storage. Then we conduct an in-depth
systematic analysis and summarize the advantageous scenarios of the quadrants.
Based on the analysis, we further propose a novel distributed GBDT system named
Vero, which adopts the unexplored composition of vertical partitioning and
row-store and suits for many large-scale cases. To validate our analysis
empirically, we implement different quadrants in the same code base and compare
them under extensive workloads, and finally compare Vero with other
state-of-the-art systems over a wide range of datasets. Our theoretical and
experimental results provide a guideline on choosing a proper data management
policy for a given workload.
