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Relational Joins on GPUs: A Closer Look
http://hdl.handle.net/2237/27090
http://hdl.handle.net/2237/270901cc26aa0-9bc4-43d9-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 (6.7 MB)
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| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||
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| 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× Anh, Nguyen× Shinpei, Kato× Masato, Edahiro× Hideyuki, Kawashima |
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| 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 | ||||||
| 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.” | |||||
| 言語 | en | |||||
| 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 | ||||||
| 内容記述タイプ | Abstract | |||||
| 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 | |||||
| 出版者 | ||||||
| 出版者 | IEEE | |||||
| 言語 | en | |||||
| 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 |
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| 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 | |||||
