"The art of balance: a RateupDB experience of building a CPU/GPU hybrid database product" Rubao Lee, Minghong Zhou, Chi Li, Shenggang Hu, Jianping Teng, Dongyang Li, and Xiaodong Zhang Proceedings of the VLDB endowment, Vol. 14, No. 12, pp. 2999-3013, August 2021. Abstract
GPU-accelerated database systems have been studied for more than 10 years, ranging from prototyping development to industry products serving in multiple domains of data applications. Existing GPU database research solutions are often focused on specific aspects in parallel algorithms and system implementations for specific features, while industry product development generally concentrates on delivering a whole system by considering its holistic performance and cost. Aiming to fill this gap between academic research and industry development, we present a comprehensive industry product study on a complete CPU/GPU HTAP system, called RateupDB. We firmly believe “the art of balance" addresses major issues in the development of RateupDB. Specifically, we consider balancing multiple factors in the software development cycle, such as the trade-off between OLAP and OLTP, the trade-off between system performance and development productivity, and balanced choices of algorithms in the product. We also present RateupDB’s complete TPC-H test performance to demonstrate its significant advantages over other existing GPU DBMS products.