PREDATOR: Predictive False Sharing Detection

False sharing is a notorious problem for multithreaded ap-plications that can drastically degrade both performance and scalability. Existing approaches can precisely identify the sources of false sharing, but only report false sharing actu-ally observed during execution; they do not generalize across executions. Because false sharing is extremely sensitive to object layout, these detectors can easily miss false sharing problems that can arise due to slight differences in mem-ory allocation order or object placement decisions by the compiler. In addition, they cannot predict the impact of false sharing on hardware with different cache line sizes. This paper presents PREDATOR, a predictive software-based false sharing detector. PREDATOR generalizes from a single execution to precisely predict false sharing that is latent in the current execution. PREDATOR tracks accesses within a range that could lead to false sharing given different object placement. It also tracks accesses within virtual cache lines, contiguous memory ranges that span actual hardware cache lines, to predict sharing on hardware platforms with larger cache line sizes. For each, it reports the exact pro-gram location of predicted false sharing problems, ranked by their projected impact on performance. We evaluate PREDA-TOR across a range of benchmarks and actual applications. PREDATOR identifies problems undetectable with previous tools, including two previously-unknown false sharing prob-lems, with no false positives. PREDATOR is able to immedi-ately locate false sharing problems in MySQL and the Boost library that had eluded detection for years