Random Forests over normalized data in CPU-GPU DBMSes

This short paper studies query execution based on message passing on CPU-GPU systems, using random forests training as the workload. We investigate different data placement and query execution strategies and find that the unique properties of training ML models using message passing necessitates different design decisions. We show that with proper data placement and CPU-GPU co-execution, training random forest models using pure SQL can outperform the leading LightGBM ML library by 1.5x on SSB SF=10.