ログイン
言語:

WEKO3

  • トップ
  • コミュニティ
  • ランキング
AND
To
lat lon distance
To

Field does not validate



インデックスリンク

インデックスツリー

メールアドレスを入力してください。

WEKO

One fine body…

WEKO

One fine body…

アイテム

{"_buckets": {"deposit": "6d6da140-2473-4494-926b-9b13879d41f3"}, "_deposit": {"id": "24870", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "24870"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00024870"}, "item_10_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2017-09-01", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "9", "bibliographicPageEnd": "2673", "bibliographicPageStart": "2663", "bibliographicVolumeNumber": "28", "bibliographic_titles": [{"bibliographic_title": "IEEE Transactions on Parallel and Distributed Systems"}]}]}, "item_10_description_4": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "The problem of scaling out relational join performance for large data sets in the database management system (DBMS) has\nbeen studied for years. Although in-memory DBMS engines can reduce load times by storing data in the main memory, join queries still\nremain computationally expensive. Modern graphics processing units (GPUs) provide massively parallel computing and may enhance\nthe performance of such join queries; however, it is not clear yet in what condition relational joins perform well on GPUs. In this paper,\nwe identify the performance characteristics of GPU computing for relational joins by implementing several well-known GPU-based join\nalgorithms under various configurations. Experimental results indicate that the speedup ratio of GPU-based relational joins to\nCPU-based counterparts depends on the number of compute cores, the size of data sets, join conditions, and join algorithms. In the\nbest 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\nexecution time of GPU-based implementation for index joins, on the other hand, is only about 0.696 times less than the execution time\nof the CPU\u2019s counterparts.", "subitem_description_type": "Abstract"}]}, "item_10_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "DOI", "subitem_identifier_uri": "http://doi.org/10.1109/TPDS.2017.2677451"}, {"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/27090"}]}, "item_10_publisher_32": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "IEEE"}]}, "item_10_rights_12": {"attribute_name": "\u6a29\u5229", "attribute_value_mlt": [{"subitem_rights": "\u201c\u00a9 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.\u201d"}]}, "item_10_select_15": {"attribute_name": "\u8457\u8005\u7248\u30d5\u30e9\u30b0", "attribute_value_mlt": [{"subitem_select_item": "author"}]}, "item_10_source_id_7": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "1045-9219", "subitem_source_identifier_type": "ISSN"}]}, "item_creator": {"attribute_name": "\u8457\u8005", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Makoto, Yabuta"}], "nameIdentifiers": [{"nameIdentifier": "73986", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Anh, Nguyen"}], "nameIdentifiers": [{"nameIdentifier": "73987", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Shinpei, Kato"}], "nameIdentifiers": [{"nameIdentifier": "73988", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Masato, Edahiro"}], "nameIdentifiers": [{"nameIdentifier": "73989", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Hideyuki, Kawashima"}], "nameIdentifiers": [{"nameIdentifier": "73990", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "\u30d5\u30a1\u30a4\u30eb\u60c5\u5831", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2018-02-22"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "Relational_Joins_on_GPUs_A_Closer_Look.pdf", "filesize": [{"value": "6.7 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 6700000.0, "url": {"label": "Relational_Joins_on_GPUs_A_Closer_Look.pdf", "url": "https://nagoya.repo.nii.ac.jp/record/24870/files/Relational_Joins_on_GPUs_A_Closer_Look.pdf"}, "version_id": "40a8a389-f4ea-49c5-bc4d-b053d1eff89f"}]}, "item_keyword": {"attribute_name": "\u30ad\u30fc\u30ef\u30fc\u30c9", "attribute_value_mlt": [{"subitem_subject": "Graphics processors", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Query processing", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Parallelism and concurrency", "subitem_subject_scheme": "Other"}]}, "item_language": {"attribute_name": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "Relational Joins on GPUs: A Closer Look", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "Relational Joins on GPUs: A Closer Look"}]}, "item_type_id": "10", "owner": "1", "path": ["312/313/314"], "permalink_uri": "http://hdl.handle.net/2237/27090", "pubdate": {"attribute_name": "\u516c\u958b\u65e5", "attribute_value": "2017-11-09"}, "publish_date": "2017-11-09", "publish_status": "0", "recid": "24870", "relation": {}, "relation_version_is_last": true, "title": ["Relational Joins on GPUs: A Closer Look"], "weko_shared_id": null}
  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
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
著者 Makoto, Yabuta

× Makoto, Yabuta

WEKO 73986

Makoto, Yabuta

Search repository
Anh, Nguyen

× Anh, Nguyen

WEKO 73987

Anh, Nguyen

Search repository
Shinpei, Kato

× Shinpei, Kato

WEKO 73988

Shinpei, Kato

Search repository
Masato, Edahiro

× Masato, Edahiro

WEKO 73989

Masato, Edahiro

Search repository
Hideyuki, Kawashima

× Hideyuki, Kawashima

WEKO 73990

Hideyuki, Kawashima

Search repository
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.”
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.
内容記述タイプ Abstract
出版者
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
ISSN
収録物識別子タイプ ISSN
収録物識別子 1045-9219
書誌情報 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
戻る
0
views
See details
Views

Versions

Ver.1 2021-03-01 13:51:25.957972
Show All versions

Share

Mendeley CiteULike Twitter Facebook Print Addthis

Cite as

Export

OAI-PMH
  • OAI-PMH JPCOAR
  • OAI-PMH DublinCore
  • OAI-PMH DDI
Other Formats
  • JSON
  • BIBTEX

Confirm


Powered by CERN Data Centre & Invenio


Powered by CERN Data Centre & Invenio