Relational joins on GPUs: A closer look

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

研究成果: Article査読

16 被引用数 (Scopus)


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.

ジャーナルIEEE Transactions on Parallel and Distributed Systems
出版ステータスPublished - 2017 9月 1

ASJC Scopus subject areas

  • 信号処理
  • ハードウェアとアーキテクチャ
  • 計算理論と計算数学


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