Low-Level Detection Of Language-Level Data Races With Lard

Researchers have proposed always-on data-race exceptions as a way to avoid the ill effects of data races, but slow performance of accurate dynamic data-race detection remains a barrier to the adoption of always-on data-race exceptions. Proposals for accurate low-level (e.g., hardware) data-race detection have the potential to reduce this performance barrier. This paper explains why low-level data-race detectors are wrong for programs written in high-level languages (e.g., Java): they miss true data races and report false data races in these programs. To bring the benefits of low-level data-race detection to high-level languages, we design low-level abstractable race detection (LARD), an extension of the interface between low-level data-race detectors and run-time systems that enables accurate language-level data-race detection using low-level detection mechanisms. We implement accurate LARD data-race exception support for Java, coupling a modified Jikes RVM Java virtual machine and a simulated hardware race detector. We evaluate our detector's accuracy against an accurate dynamic Java data-race detector and other low-level race detectors without LARD, showing that naive accurate low-level data-race detectors suffer from many missed and false language-level races in practice, and that LARD prevents this inaccuracy.