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  1. A500 情報学部/情報学研究科・情報文化学部・情報科学研究科
  2. A500a 雑誌掲載論文
  3. 学術雑誌

Relational Joins on GPUs: A Closer Look

http://hdl.handle.net/2237/27090
http://hdl.handle.net/2237/27090
1cc26aa0-9bc4-43d9-a4dc-46b14883de28
名前 / ファイル ライセンス アクション
Relational_Joins_on_GPUs_A_Closer_Look.pdf Relational_Joins_on_GPUs_A_Closer_Look.pdf (6.7 MB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2017-11-09
タイトル
タイトル Relational Joins on GPUs: A Closer Look
言語 en
著者 Makoto, Yabuta

× Makoto, Yabuta

WEKO 73986

en Makoto, Yabuta

Search repository
Anh, Nguyen

× Anh, Nguyen

WEKO 73987

en Anh, Nguyen

Search repository
Shinpei, Kato

× Shinpei, Kato

WEKO 73988

en Shinpei, Kato

Search repository
Masato, Edahiro

× Masato, Edahiro

WEKO 73989

en Masato, Edahiro

Search repository
Hideyuki, Kawashima

× Hideyuki, Kawashima

WEKO 73990

en Hideyuki, Kawashima

Search repository
アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
権利
言語 en
権利情報 “© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
キーワード
主題Scheme Other
主題 Graphics processors
キーワード
主題Scheme Other
主題 Query processing
キーワード
主題Scheme Other
主題 Parallelism and concurrency
抄録
内容記述 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.
言語 en
内容記述タイプ Abstract
出版者
言語 en
出版者 IEEE
言語
言語 eng
資源タイプ
資源タイプresource http://purl.org/coar/resource_type/c_6501
タイプ journal article
出版タイプ
出版タイプ AM
出版タイプResource http://purl.org/coar/version/c_ab4af688f83e57aa
DOI
関連タイプ isVersionOf
識別子タイプ DOI
関連識別子 https://doi.org/10.1109/TPDS.2017.2677451
ISSN
収録物識別子タイプ PISSN
収録物識別子 1045-9219
書誌情報 en : IEEE Transactions on Parallel and Distributed Systems

巻 28, 号 9, p. 2663-2673, 発行日 2017-09-01
著者版フラグ
値 author
URI
識別子 http://doi.org/10.1109/TPDS.2017.2677451
識別子タイプ DOI
URI
識別子 http://hdl.handle.net/2237/27090
識別子タイプ HDL
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