GPL: A GPU-based Pipelined Query Processing Engine

Graphics Processing Units (GPUs) have evolved as a powerful query co-processor for main memory On-Line Analytical Processing (OLAP) databases. However, existing GPU-based query processors adopt a kernel-based execution approach which optimizes individual kernels for resource utilization and executes the GPU kernels involved in the query plan one by one. Such a kernel-based approach cannot utilize all GPU resources efficiently due to the resource underutilization of individual kernels and memory ping-pong across kernel executions. In this paper, we propose GPL, a novel pipelined query execution engine to improve the resource utilization of query co-processing on the GPU. Different from the existing kernel-based execution, GPL takes advantage of hardware features of new-generation GPUs including concurrent kernel execution and efficient data communication channel between kernels. We further develop an analytical model to guide the generation of the optimal pipelined query plan. Thus, the tile size of the pipelined query execution can be adapted in a cost-based manner. We evaluate GPL with TPC-H queries on both AMD and NVIDIA GPUs. The experimental results show that 1) the analytical model is able to guide determining the suitable parameter values in pipelined query execution plan, and 2) GPL is able to significantly outperform the state-of-the-art kernel-based query processing approaches, with improvement up to 48%.