In-Cache Query Co-Processing on Coupled CPU-GPU Architectures

Recently, there have been some emerging processor designs that the CPU and the GPU (Graphics Processing Unit) are integrated in a single chip and share Last Level Cache (LLC). However, the main memory bandwidth of such cou-pled CPU-GPU architectures can be much lower than that of a discrete GPU. As a result, current GPU query co-processing paradigms can severely suffer from memory stalls. In this paper, we propose a novel in-cache query co-processing paradigm for main memory On-Line Analytical Process-ing (OLAP) databases on coupled CPU-GPU architectures. Specifically, we adapt CPU-assisted prefetching to minimize cache misses in GPU query co-processing and CPU-assisted decompression to improve query execution performance. Fur-thermore, we develop a cost model guided adaptation mech-anism for distributing the workload of prefetching, decom-pression, and query execution between CPU and GPU. We implement a system prototype and evaluate it on two recent AMD APUs A8 and A10. The experimental results show that 1) in-cache query co-processing can effectively improve the performance of the state-of-the-art GPU co-processing paradigm by up to 30 % and 33 % on A8 and A10, respec-tively, and 2) our workload distribution adaption mechanism can significantly improve the query performance by up to 36 % and 40 % on A8 and A10, respectively. 1