Optimizing parallel join of column-stores on heterogeneous computing platforms

摘要

GPU and integrated multi-core CPU-GPU architecture has powerful parallel processing capability and programmable pipeline, which gradually becomes a hot area of database researches. In order to fully explore the potential abilities of heterogeneous platform, enhancing the query performance of the column-storage database. In this paper, we are taking full account of differences among system architecture based on heterogeneous platforms, firstly proposed data partition strategy for join operation on multi-thread platform-ICMD algorithm, using stream processor to process sub-space join operation in parallel. Secondly, through the implementation of query dynamic load by using task allocation model evaluation, which makes the query execution in parallel between multi-core CPU, GPU and other accelerator components. At the same time, the use of on-chip global synchronization and efficient implementation, local memory reuse optimized ICMD connection algorithm. Using SSB benchmark test, the experimental results show that based on the platform of Intel HD Graphics 4600, ICMD connection query received 1.35 speedup compared to the CPU version and received 18% performance improvement compared with GPU query engine-Ocelot.