Demand-driven software race detection using hardware performance counters

Abstract

Dynamic data race detectors are an important mechanism for creating robust parallel programs. Software race detectors instrument the program under test, observe each memory access, and watch for inter-thread data sharing that could lead to concurrency errors. While this method of bug hunting can find races that are normally difficult to observe, it also suffers from high runtime overheads. It is not uncommon for commercial race detectors to experience 300x slowdowns, limiting their usage. This paper presents a hardware-assisted demand-driven race detector. We are able to observe cache events that are indicative of data sharing between threads by taking advantage of hardware available on modern commercial microprocessors. We use these to build a race detector that is only enabled when it is likely that inter-thread data sharing is occurring. When little sharing takes place, this demand-driven analysis is much faster than contemporary continuous-analysis tools without a large loss of detection accuracy. We modified the race detector in Intel(R) Inspector XE to utilize our hardware-based sharing indicator and were able to achieve performance increases of 3x and 10x in two parallel benchmark suites and 51x for one particular program.