{"id":685917,"date":"2020-08-20T11:27:34","date_gmt":"2020-08-20T18:27:34","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=685917"},"modified":"2020-08-20T11:27:34","modified_gmt":"2020-08-20T18:27:34","slug":"transparent-offloading-and-mapping-tom-enabling-programmer-transparent-near-data-processing-in-gpu-systems","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/transparent-offloading-and-mapping-tom-enabling-programmer-transparent-near-data-processing-in-gpu-systems\/","title":{"rendered":"Transparent Offloading and Mapping (TOM): Enabling Programmer-Transparent Near-Data Processing in GPU Systems"},"content":{"rendered":"<p>Main memory bandwidth is a critical bottleneck for modern GPU systems due to limited off-chip pin bandwidth. 3D-stacked memory architectures provide a promising opportunity to significantly alleviate this bottleneck by directly connecting a logic layer to the DRAM layers with high bandwidth connections. Recent work has shown promising potential performance benefits from an architecture that connects multiple such 3D-stacked memories and offl\u00adoads bandwidth-intensive computations to a GPU in each of the logic layers. An unsolved key challenge in such a system is how to enable computation o\u00adffloading and data mapping to multiple 3D-stacked memories without burdening the programmer such that any application can transparently benefit from near-data processing capabilities in the logic layer.<\/p>\n<p>Our paper develops two new mechanisms to address this key challenge. First, a compiler-based technique that automatically identifies code to offl\u00adoad to a logic-layer GPU based on a simple cost-benefit analysis. Second, a software\/hardware cooperative mechanism that predicts which memory pages will be accessed by offl\u00adoaded code, and places those pages in the memory stack closest to the offl\u00adoaded code, to minimize off-chip bandwidth consumption. We call the combination of these two programmer-transparent mechanisms TOM: Transparent Offl\u00adoading and Mapping.<\/p>\n<p>Our extensive evaluations across a variety of modern memory-intensive GPU workloads show that, without requiring any program modification, TOM significantly improves performance (by 30% on average, and up to 76%) compared to a baseline GPU system that cannot offl\u00adoad computation to 3D-stacked memories.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Main memory bandwidth is a critical bottleneck for modern GPU systems due to limited off-chip pin bandwidth. 3D-stacked memory architectures provide a promising opportunity to significantly alleviate this bottleneck by directly connecting a logic layer to the DRAM layers with high bandwidth connections. Recent work has shown promising potential performance benefits from an architecture that [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"International Symposium on Computer Architecture (ISCA)","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2016-6","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"http:\/\/isca2016.eecs.umich.edu\/","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":0,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":false,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13552,13547],"msr-publication-type":[193716],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-685917","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-hardware-devices","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2016-6","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":0,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/kevinhsieh.github.io\/pub\/TOM-programmer-transparent-GPU-near-date-processing_isca16.pdf","label_id":"243109","label":0}],"msr_related_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"http:\/\/isca2016.eecs.umich.edu\/wp-content\/uploads\/2016\/07\/3B-2.pdf","label_id":"243115","label":0}],"msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[],"msr-author-ordering":[{"type":"user_nicename","value":"Kevin Hsieh","user_id":39459,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Kevin Hsieh"},{"type":"text","value":"Eiman Ebrahimi","user_id":0,"rest_url":false},{"type":"text","value":"Gwangsun Kim","user_id":0,"rest_url":false},{"type":"text","value":"Niladrish Chatterjee","user_id":0,"rest_url":false},{"type":"text","value":"Mike O&#39;Connor","user_id":0,"rest_url":false},{"type":"text","value":"Nandita Vijaykumar","user_id":0,"rest_url":false},{"type":"text","value":"Onur Mutlu","user_id":0,"rest_url":false},{"type":"text","value":"Stephen W. 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