"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.