{"id":162682,"date":"2012-06-01T00:00:00","date_gmt":"2012-06-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/managing-large-graphs-on-multi-cores-with-graph-awareness\/"},"modified":"2018-10-16T20:46:28","modified_gmt":"2018-10-17T03:46:28","slug":"managing-large-graphs-on-multi-cores-with-graph-awareness","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/managing-large-graphs-on-multi-cores-with-graph-awareness\/","title":{"rendered":"Managing Large Graphs on Multi-Cores with Graph Awareness"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Grace is a graph-aware, in-memory, transactional graph management system, specifically built for real-time queries and fast iterative computations. It is designed to run on large multi-cores, taking advantage of the inherent parallelism to improve its performance. Grace contains a number of graph-specific and multi-core-specific optimizations including graph partitioning, careful in-memory vertex ordering, updates batching, and load-balancing. It supports queries, searches, iterative computations, and transactional updates. Grace scales to large graphs (e.g., a Hotmail graph with 320 million vertices) and performs up to two orders of magnitude faster than commercial key-value stores and graph databases.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Grace is a graph-aware, in-memory, transactional graph management system, specifically built for real-time queries and fast iterative computations. It is designed to run on large multi-cores, taking advantage of the inherent parallelism to improve its performance. Grace contains a number of graph-specific and multi-core-specific optimizations including graph partitioning, careful in-memory vertex ordering, updates batching, and [&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":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13547],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-162682","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"USENIX","msr_edition":"USENIX Annual Technical Conference (USENIX ATC'12)","msr_affiliation":"","msr_published_date":"2012-06-01","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":1,"msr_main_download":"219274","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"grace.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2012\/06\/grace.pdf","id":219274,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":219274,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2012\/06\/grace.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"vijayanp","user_id":34582,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=vijayanp"},{"type":"user_nicename","value":"miw","user_id":32960,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=miw"},{"type":"text","value":"Xuetian Weng","user_id":0,"rest_url":false},{"type":"user_nicename","value":"mcsherry","user_id":32863,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=mcsherry"},{"type":"user_nicename","value":"lidongz","user_id":32673,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=lidongz"},{"type":"text","value":"Maya Haridasan","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[510017,144927],"msr_project":[170955],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":170955,"post_title":"Graph Storage and Analysis","post_name":"temporal-graph-storage-and-analysis-of-social-data","post_type":"msr-project","post_date":"2012-05-17 23:28:59","post_modified":"2020-04-20 22:48:07","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/temporal-graph-storage-and-analysis-of-social-data\/","post_excerpt":"An explosion of user-generated data from online social networks motivates analysis to extract deep insights from this data's graph at scale, even of social, temporal, spatial, and topical connections. We are building systems to enable storage and analysis of such graphs that considers characteristics such as evolution over time when trending topics or social activities change. 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