{"id":707827,"date":"2020-11-24T09:37:07","date_gmt":"2020-11-24T17:37:07","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=707827"},"modified":"2021-03-14T08:06:08","modified_gmt":"2021-03-14T15:06:08","slug":"kard-lightweight-data-race-detection-with-per-thread-memory-protection","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/kard-lightweight-data-race-detection-with-per-thread-memory-protection\/","title":{"rendered":"KARD: Lightweight Data Race Detection with Per-Thread Memory Protection"},"content":{"rendered":"<p>Finding data race bugs in multi-threaded programs has proven challenging. A promising direction is to use dynamic detectors that monitor the program&#8217;s execution for data races. However, despite extensive work on dynamic data race detection, most proposed systems for commodity<br \/>\nhardware incur prohibitive overheads due to expensive compiler instrumentation of memory accesses; hence, they are not efficient enough to be used in all development and testing settings.<\/p>\n<p>KARD is a lightweight system that dynamically detects data races caused by inconsistent lock usage&#8212;when a program concurrently accesses the same memory object using different locks or only some of the concurrent accesses are synchronized using a common lock. Unlike existing detectors, KARD does not monitor memory accesses using expensive compiler instrumentation. Instead, KARD leverages commodity per-thread memory protection, Intel Memory Protection Keys (MPK). Using MPK, KARD ensures that a shared object is only accessible to a single thread in its critical section, and captures all violating accesses from other concurrent threads. KARD overcomes various limitations of MPK by introducing key-enforced race detection, employing consolidated unique page allocation, carefully managing protection keys, and automatically pruning out non-racy or redundant violations. Our evaluation shows that KARD detects all data races caused by inconsistent lock usage and has a low geometric mean execution time overhead: 7.0% on PARSEC and SPLASH-2x benchmarks and 5.3% on a set of real-world applications (NGINX, memcached, pigz, and Aget).<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Finding data race bugs in multi-threaded programs has proven challenging. A promising direction is to use dynamic detectors that monitor the program&#8217;s execution for data races. However, despite extensive work on dynamic data race detection, most proposed systems for commodity hardware incur prohibitive overheads due to expensive compiler instrumentation of memory accesses; hence, they are [&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 Conference on Architectural Support for Programming Languages and Operating Systems 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