ReProVide: Query Optimization and Near-Data Processing on Reconfigurable SoCs for Big Data Analysis
Tobias Hahn, Maximilian S. Langohr, Andreas Becher, Lekshmi Beena Gopalakrishnan Nair, Klaus Meyer-Wegener, Jürgen Teich, Stefan Wildermann · 2025
Abstract The available parallelism and heterogeneity of emerging computer systems must be exploited for being able to process the huge amounts of data produced every day. As a consequence, we observe an increasing research interest in accelerating database query processing on multi-cores and attached co-processors like Graphics Processing Units (GPUs) and Field-Programmable Gate Arrays (FPGAs). This chapter presents ReProVide, an approach combining near-data processing and FPGA-based acceleration. The System-on-Chip (SoC) architecture of ReProVide including a flexibly reconfigurable FPGA can load and execute hardware accelerators for various operators on relational and streaming data. Moreover, we present novel DBMS techniques for partitioning query-execution plans between a host and Reconfigurable data-Provider Units (RPUs) and for mapping operators onto RPUs by means of hardware reconfiguration.