{"_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_name_i18n": "\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}