关键词: CPU-GPU异构计算平台, 向量分组聚集, 分组向量索引, 数据密集型负载

Abstract:

The grouping & aggregation operation is one of the important OLAP operator, and it is data-intensive workload. In main-memory database and GPU database scenarios, not only the performance optimizations are to be studied but also how to optimally assign the executing platform for grouping & aggregation operation to minimize data transmission overhead between CPU and GPU should be focused. This paper presents vector grouping aggregation method, the traditional grouping & aggregation operation is separated from the pipeline by “early grouping, late aggregating” strategy, so that the data-intensive grouping & aggregation operation is separated from the pipeline. Moreover, the optimal workload distribution is achieved by matching processor hardware characteristics with algorithm pattern. Vector grouping aggregation achieves dramatically performance improvements against traditional hash based grouping aggregation operation. The experimental results show that the maximal performance gains between vector grouping aggregation algorithm against the leading main-memory database Hyper and GPU database MapD achieves 5~8 times improvements. The vector grouping aggregation approach not only improves the performance of OLAP aggregation, but also separates the data-intensive workload from query plan. The heterogeneous computing platform can optimally configure the computing resources according to hardware characteristics to improve the overall OLAP performance with hybrid processors.

Key words: CPU-GPU heterogeneous computing platform, vector grouping &, aggregation, group vector index, computing-intensive workload