Relational joins on GPUs: A closer look

Makoto Yabuta, Anh Nguyen, Shinpei Kato, Masato Edahiro, Hideyuki Kawashima

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

The problem of scaling out relational join performance for large data sets in the database management system (DBMS) has been studied for years. Although in-memory DBMS engines can reduce load times by storing data in the main memory, join queries still remain computationally expensive. Modern graphics processing units (GPUs) provide massively parallel computing and may enhance the performance of such join queries; however, it is not clear yet in what condition relational joins perform well on GPUs. In this paper, we identify the performance characteristics of GPU computing for relational joins by implementing several well-known GPU-based join algorithms under various configurations. Experimental results indicate that the speedup ratio of GPU-based relational joins to CPU-based counterparts depends on the number of compute cores, the size of data sets, join conditions, and join algorithms. In the best case, the speedup ratios are up to 6.67 times for non-index joins, 9.41 times for sort index joins, and 2.55 times for hash joins. The execution time of GPU-based implementation for index joins, on the other hand, is only about 0.696 times less than the execution time of the CPU's counterparts.

Original languageEnglish
Article number7869389
Pages (from-to)2663-2673
Number of pages11
JournalIEEE Transactions on Parallel and Distributed Systems
Volume28
Issue number9
DOIs
Publication statusPublished - 2017 Sep 1
Externally publishedYes

Keywords

  • Graphics processors
  • parallelism and concurrency
  • query processing

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics

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