Sailfish: Exploring Heterogeneous Query Acceleration on Discrete CPU-FPGA Architecture

The hardware of modern server is being increasingly heterogeneous as advanced accelerators, such as FPGAs, are used together with multicore CPUs to meet the computing requirement of analytical query workloads. Unfortunately, the earlier database engines are designed for homogeneous servers, where query execution is only parallelized across CPUs, but ignores the prized FPGA resources. To exploit the available heterogeneous resources, emerging work try to construct the cross-device query pipeline, wherein a part of the operators run on the FPGA-end and the rest run on the CPU-end respectively. However, when running such a pipeline, the CPU-end and FPGA-end operators could obtain mismatched computing resources, which easily cause uneven processing performance between two ends, thereby limiting whole pipeline performance or wasting computing resources. Of note, it is nearly impossible for query optimizers to assign the matched computing resources to make CPU-end and FPGA-end have the similar processing performance, as several factors (e.g., operator selectivity) affect processing performance. To tackle this problem, we propose the heterogeneous motion operator, which can adjust the runtime computing resources (e.g., parallelism) of CPU-end, so as to match FPGA-end processing performance. In addition, we further implement an FPGA accelerator that supports parallel processing of hash join. By integrating our motion operator into PostgreSQL with the above FPGA accelerator, we build a prototype called Sailfish, whose experimental performance exceeds the native acceleration scheme by a huge margin.