Column-stored system join optimization on coupled CPU-GPU architecture

Heterogeneous architecture is the new trend of the development of Computer system central processor unit (CPU). Taking advantage of its powerful computer power has been a new research hotspot in database system field. First, in order to enhance the query performance of column-oriented database, we propose a data partition model which is environment sensitive. The data partition model provides optimal data division for every processing unit dynamically, by monitoring the CPU occupancy rate. Then, for GPU memory access optimization, we propose a DFAT estimate model for prefetching. At the same time, we optimize GPU memory access based on coalesced access; We implement a sort-merge join algorithm on a PC with an integrated CPU-GPU chip, which adopts our data partition model and our cost model in prefetching. Our strategy is able to distribute data to different processing units automatically, and can make sort-merge join achieve a performance improvement of 33% on coupled CPU-GPU architecture.