FPGA for aggregate processing: The good, the bad, and the ugly

Zubeyr F. Eryilmaz, Aarati Kakaraparthy, Jignesh M. Patel, Rathijit Sen, Kwanghyun Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

In this paper, we focus on current CPU-FPGA architectures and study their usability for database management systems. To focus our scope, we choose aggregation as the query processing primitive for this investigation. We implement a fully pipelined stall-free module that performs aggregation on the FPGA, and also describe a performance model that predicts the runtime of this module with 99% accuracy. We study the performance of this module on two different CPU-FPGA architectures, namely remote-main-memory and bump-in-the-wire. Compared to an implementation of aggregation on CPU, we find that the former is 1.7× slower whereas the latter is 2.2× faster. This significant performance gap suggests two important architectural considerations when designing CPU-FPGA systems, namely the bandwidth ceiling and the resource ceiling, while also highlighting issues of switching times and programmer efficiency. We consider broader hardware trends to study the suitability of the two FPGA architectures for accelerating the aggregation operation, and find that the performance gap is likely to stay in the coming future. Based on these observations, we discuss some challenges and opportunities for CPU-FPGA architectures.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021
PublisherIEEE Computer Society
Pages1044-1055
Number of pages12
ISBN (Electronic)9781728191843
DOIs
Publication statusPublished - 2021 Apr
Event37th IEEE International Conference on Data Engineering, ICDE 2021 - Virtual, Chania, Greece
Duration: 2021 Apr 192021 Apr 22

Publication series

NameProceedings - International Conference on Data Engineering
Volume2021-April
ISSN (Print)1084-4627

Conference

Conference37th IEEE International Conference on Data Engineering, ICDE 2021
Country/TerritoryGreece
CityVirtual, Chania
Period21/4/1921/4/22

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Information Systems

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