{"id":183364,"date":"2006-08-23T00:00:00","date_gmt":"2009-10-31T12:40:49","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/race-aware-runtimes-for-concurrent-software-development-deployment\/"},"modified":"2016-09-09T09:45:34","modified_gmt":"2016-09-09T16:45:34","slug":"race-aware-runtimes-for-concurrent-software-development-deployment","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/race-aware-runtimes-for-concurrent-software-development-deployment\/","title":{"rendered":"Race-aware Runtimes for Concurrent Software Development & Deployment"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We will present ongoing research on a programming framework in which undesirable data races raise runtime exceptions the same way out-of-bounds array accesses or null-pointer dereferences do in Java. Race exceptions provide a mechanism for detecting and handling races before they lead to serious errors such as security violations or data corruption. The absence of a race exception rules out a race condition as the source of a bug.<\/p>\n<p>A key component of this framework is Goldilocks, a novel algorithm for precise and efficient race detection at runtime. Goldilocks (presented at FATES\/RV &#8217;06) is based on locksets and is as efficient as other lockset-based algorithms. Unlike them, it is not specific to a particular synchronization discipline. It computes the happens-before relation like vector clocks do and is therefore sound and precise. Goldilocks was implemented inside the Kaffe JVM.  Experiments show that lazy evaluation of locksets and certain short-circuits make Goldilocks very efficient.<\/p>\n<p>In the second part of the talk, we will present a proposal for the rest of the programming framework. Key features of the framework are<\/p>\n<ul>\n<li>Mechanisms to handle races programmatically at runtime without affecting the operation of the program<\/li>\n<li>A tight integration of static and dynamic analyses to make continuous monitoring for race and other concurrency exceptions feasible<\/li>\n<li>Insertion into Java byte-code (composable) results of static analyses for the runtime to more efficiently monitor data races<\/li>\n<\/ul>\n<p>This work is a collaboration between Koc University and MSR.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We will present ongoing research on a programming framework in which undesirable data races raise runtime exceptions the same way out-of-bounds array accesses or null-pointer dereferences do in Java. Race exceptions provide a mechanism for detecting and handling races before they lead to serious errors such as security violations or data corruption. The absence of [&hellip;]<\/p>\n","protected":false},"featured_media":195021,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_hide_image_in_river":0,"footnotes":""},"research-area":[],"msr-video-type":[],"msr-locale":[268875],"msr-post-option":[],"msr-session-type":[],"msr-impact-theme":[],"msr-pillar":[],"msr-episode":[],"msr-research-theme":[],"class_list":["post-183364","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/a4rSE9UcpRg","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/183364","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-video"}],"version-history":[{"count":0,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/183364\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/195021"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=183364"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=183364"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=183364"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=183364"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=183364"},{"taxonomy":"msr-session-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-session-type?post=183364"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=183364"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=183364"},{"taxonomy":"msr-episode","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-episode?post=183364"},{"taxonomy":"msr-research-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-theme?post=183364"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}