swGBDT: Efficient Gradient Boosted Decision Tree on Sunway Many-Core Processor

Gradient Boosted Decision Trees (GBDT) is a practical machine learning method, which has been widely used in various application fields such as recommendation system. Optimizing the performance of GBDT on heterogeneous many-core processors exposes several challenges such as designing efficient parallelization scheme and mitigating the latency of irregular memory access. In this paper, we propose swGBDT, an efficient GBDT implementation on Sunway processor. In swGBDT, we divide the 64 CPEs in a core group into multiple roles such as loader, saver and worker in order to hide the latency of irregular global memory access. In addition, we partition the data into two granularities such as block and tile to better utilize the LDM on each CPE for data caching. Moreover, we utilize register communication for collaboration among CPEs. Our evaluation with representative datasets shows that swGBDT achieves 4.6 and 2 performance speedup on average compared to the serial implementation on MPE and parallel XGBoost on CPEs respectively.