Detecting and Surviving Data Races using Complementary Schedules

Kaushik Veeraraghavan, Peter M. Chen, Jason Flinn, and Satish Narayanasamy

Abstract

Data races are a common source of errors in multithreaded programs. In this paper, we show how to protect a program from data race errors at runtime by executing multiple replicas of the program with complementary thread schedules. Complementary schedules are a set of replica thread schedules crafted to ensure that replicas diverge only if a data race occurs and to make it very likely that harmful data races cause divergences. Our system, called Frost, uses complementary schedules to cause at least one replica to avoid the order of racing instructions that leads to incorrect program execution for most harmful data races. Frost introduces outcome-based race detection, which detects data races by comparing the state of replicas executing complementary schedules. We show that this method is substantially faster than existing dynamic race detectors for unmanaged code. To help programs survive bugs in production, Frost also diagnoses the data race bug and selects an appropriate recovery strategy, such as choosing a replica that is likely to be correct or executing more replicas to gather additional information.

Frost controls the thread schedules of replicas by running all threads of a replica non-preemptively on a single core. To scale the program to multiple cores, Frost runs a third replica in parallel to generate checkpoints of the program's likely future states; these checkpoints let Frost divide program execution into multiple epochs, which it then runs in parallel.

We evaluate Frost using 11 real data race bugs in desktop and server applications. Frost both detects and survives all of these data races. Since Frost runs three replicas, its utilization cost is 3x. However, if there are spare cores to absorb this increased utilization, Frost adds only 3--12% overhead to application runtime.