Domain-specific architecture has greatly improved performance and energy efficiency in in-memory databases, especially for accelerating single-functional computing logic in analytic query processing, such as sort, join and aggregation. However, as data volumes surge exponentially, these dedicated accelerators are struggling to satisfy the burgeoning demand for handling intricate and multifaceted workloads. A major challenge lies in establishing a flexible framework that engages these ‘coarse-grained’ units without incurring extra overheads from hardware integration, programming, compilation, runtime and operating systems.In this paper, the kernel processing unit (KPU) is proposed to optimize CPU-accelerator heterogeneous systems for in-memory databases. KPU provides a unified interface to consolidate all database query operators. In terms of KPU hardware architecture, kernel customization and data transmission are two critical bottlenecks. To address the challenges, multiple independently designed homogeneous table cores are integrated to support flexible high-performance SQL queries, and a customized efficient data management system (DMS) works collaboratively to maximize the utilization of on-chip memory bandwidth. Additionally, a database application-specific KPU instruction set architecture (KISA) dedicated to parallel analytical query processing is proposed to enable parallel KPU programming. To trade off between accelerator computing capacity and data transfer latency, KPU designs an offloading mechanism to map SQL queries between the CPU and accelerator adaptively based on a performance model and a function simulator. The experiments demonstrate that KPU surpasses the general-purpose CPU and GPU by an average of 24.5× and 8.75×, respectively.

Application-specific accelerator is a prominent way for analytic query processing. To achieve a substantial improvement over the state-of-the-art in performance while maintaining programmability, we propose a kernel processing unit (KPU) framework and apply it to SQL acceleration. Kernel customization and data transmission are two critical bottlenecks, we separately optimize them in the key core and shadow core with a self-designed data management system. A software stack named RACE with a performance model and function simulator is also introduced. The experiments demonstrate that KPU-SQL outperforms the CPU and GPU by 24.5x and 8.75x on average, respectively.