Though field-programmable gate arrays (FPGAs) have been used to accelerate database systems, they have not been widely adopted for the following reasons. As databases have transitioned to higher bandwidth technology such as in-memory and NVMe, the communication overhead associated with accelerators has become more of a burden. Also, FPGAs are more difficult to program, and GPUs have emerged as an alternative technology with better programming support. However, with the development of new interconnect technology, memory technology, and improved FPGA design tool chains, FPGAs again provide significant opportunities. Therefore, we believe that FPGAs can be attractive again in the database field. This thesis focuses on FPGAs as a high-performance compute platform, and explores using FPGAs to accelerate database systems. It investigates the current challenges that have held FPGAs back in the database field as well as the opportunities resulting from recent technology developments. The investigation illustrates that FPGAs can provide significant advantages for integration in database systems. However, to make further progress, studies in a number of areas, including new database architectures, new types of accelerators, deep performance analysis, and the development of the tool chains are required. Our contributions focus on accelerators for databases implemented in reconfigurable logic. We provide an overview of prior work and make contributions to two specific types of accelerators: both a compute-intensive (decompression) and a memory-intensive (hash join) accelerator.
|Qualification||Doctor of Philosophy|
|Award date||10 Dec 2019|
|Publication status||Published - 2019|