{"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&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":"
Finding data race bugs in multi-threaded programs has proven challenging. A promising direction is to use dynamic detectors that monitor the program’s execution for data races. However, despite extensive work on dynamic data race detection, most proposed systems for commodity
\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
KARD is a lightweight system that dynamically detects data races caused by inconsistent lock usage—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":"
Finding data race bugs in multi-threaded programs has proven challenging. A promising direction is to use dynamic detectors that monitor the program’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 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