{"id":976416,"date":"2023-10-16T02:19:42","date_gmt":"2023-10-16T09:19:42","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=976416"},"modified":"2023-10-16T02:19:42","modified_gmt":"2023-10-16T09:19:42","slug":"a-case-for-graphics-driven-query-processing","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/a-case-for-graphics-driven-query-processing\/","title":{"rendered":"A Case for Graphics-Driven Query Processing"},"content":{"rendered":"
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Over the past decade, the database research community has directed considerable attention towards harnessing the power of GPUs in query processing engines. The proposed techniques have primarily focused on devising customized low-level mechanisms that utilize the raw hardware parallelism provided abundantly by GPU compute kernels.<\/p>\n

In this paper, we advocate a radically different approach – instead of dealing directly with hardware idiosyncrasies, to leverage the well-established graphics pipeline architecture<\/i> baked into the GPU hardware. A variety of advantages accrue from this high-level abstraction: (a) Extracting the power of GPUs is outsourced to highly-optimized graphics drivers, thereby providing hardware-consciousness for free; (b) Query processing becomes agnostic to changes in GPU architectures (e.g. integrated vs discrete) and vendors, requiring only a change of drivers; (c) Contemporary graphics APIs also support a compute element, facilitating query operator designs that seamlessly straddle the compute and graphics worlds.<\/p>\n

As a proof of concept of the above vision, we implement here the workhorse Join and GroupBy operators using core graphics primitives. These implementations, based on the Vulkan API, have been evaluated over large benchmark databases on vanilla hybrid computing platforms. The experimental results indicate both substantive performance benefits (typically, around 2X faster) over existing approaches, as well as auto-tuned portability to new hardware platforms.<\/p>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Over the past decade, the database research community has directed considerable attention towards harnessing the power of GPUs in query processing engines. The proposed techniques have primarily focused on devising customized low-level mechanisms that utilize the raw hardware parallelism provided abundantly by GPU compute kernels. In this paper, we advocate a radically different approach – […]<\/p>\n","protected":false},"featured_media":976443,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13563],"msr-publication-type":[193715],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[248884],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-976416","msr-research-item","type-msr-research-item","status-publish","has-post-thumbnail","hentry","msr-research-area-data-platform-analytics","msr-locale-en_us","msr-field-of-study-database"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2023-8-8","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"Proceedings of the VLDB Endowment","msr_volume":"16","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"10","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":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"doi","viewUrl":"false","id":"false","title":"https:\/\/doi.org\/10.14778\/3603581.3603590","label_id":"243106","label":0},{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/10\/garuda-pvldb.pdf","id":"976425","title":"garuda-pvldb","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[{"id":976428,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/10\/garuda-pvldb-652cfc1185a6c.pdf"},{"id":976425,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/10\/garuda-pvldb.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"Harish Doraiswamy","user_id":41051,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Harish Doraiswamy"},{"type":"text","value":"Vikas Kalagi","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Karthik Ramachandra","user_id":37658,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Karthik Ramachandra"},{"type":"text","value":"Jayant R. Haritsa","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[967329],"publication":[],"video":[],"download":[],"msr_publication_type":"article","related_content":{"projects":[{"ID":967329,"post_title":"Domain Specialization","post_name":"domain-specialization","post_type":"msr-project","post_date":"2023-10-16 02:14:29","post_modified":"2024-01-12 08:47:20","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/domain-specialization\/","post_excerpt":"Scaling performance beyond Moore's law Domain specialization is expected to play a big role in how computer systems evolve in future. With the end of Moore's law, we are already seeing CPU, GPU and domain specific hardware evolving rapidly. The next decade is therefore expected to see big changes in how we develop, compile and run software. This project focuses on data systems, a class of systems where, as the data sizes grow, performance scaling is going to be of importance.First, we believe that domain-specific compilers will play a crucial strategic role in helping software leverage the changing hardware landscape. Such compilers will be multi-layered and will progressively lower computation through multiple intermediate abstractions, performing domain specific optimizations at the higher layers and specializing code to the hardware in lower layers. We have been working on two such domain specific compilers in the data domain. 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