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SQL2FPGA: Automated Acceleration of SQL Query Processing on Modern CPU-FPGA Platforms

ACM TRANSACTIONS ON reconfigurable TECHNOLOGY AND systems 2024年第3期17卷 1-28页

作者： Lu, Alec Narendra, Jahanvi Fang, Zhenman Simon Fraser Univ Sch Engn Sci Burnaby BC Canada Simon Fraser Univ Burnaby BC Canada

Today's big data query engines are constantly under pressure to keep up with the rapidly increasing demand for faster processing of more complex workloads. In the past few years, FPGA-based database acceleration efforts have demonstrated promising performance improvement with good energy efficiency. However, few studies target the programming and design automation support to leverage the FPGA accelerator benefits in query processing. Most of them rely on the SQL query plan generated by CPU query engines and manually map the query plan onto the FPGA accelerators, which is tedious and error-prone. Moreover, such CPU-oriented query plans do not consider the utilization of FPGA accelerators and could lose more optimization opportunities. In this article, we present SQL2FPGA, an FPGA accelerator-aware compiler to automatically map SQL queries onto the heterogeneous CPU-FPGA platforms. Our SQL2FPGA front-end takes an optimized logical plan of an SQL query from a database query engine and transforms it into a unified operator-level intermediate representation. To generate an optimized FPGA-aware physical plan, SQL2FPGA implements a set of compiler optimization passes to (1) improve operator acceleration coverage by the FPGA, (2) eliminate redundant computation during physical execution, and (3) minimize data transfer overhead between operators on the CPU and FPGA. Furthermore, it also leverages machine learning techniques to predict and identify the optimal platform, either CPU or FPGA, for the physical execution of individual query operations. Finally, SQL2FPGA generates the associated query acceleration code for heterogeneous CPU-FPGA system deployment. Compared to the widely used Apache Spark SQL framework running on the CPU, SQL2FPGA-using two AMD/Xilinx HBM-based Alveo U280 FPGA boards and Ver.2020 AMD/Xilinx FPGA overlay designs-achieves an average performance speedup of 10.1x and 13.9x across all 22 TPC-H benchmark queries in a scale factor of 1 GB (SF1) and 30 GB (

关键词： Hardware- Hardware accelerators Hardware-software codesign computer systems organization- reconfigurable computing High-level language architectures

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