IFRit

Abstract

We propose a new algorithm for dynamic data-race detection. Our algorithm reports no false positives and runs on arbitrary C and C++ code. Unlike previous algorithms, we do not have to instrument every memory access or track a full happens-before relation.Our data-race detector, which we call IFRit, is based on a run-time abstraction called an interference-free region (IFR). An IFR is an interval of one thread's execution during which any write to a specific variable by a different thread is a data race. We insert instrumentation at compile time to monitor active IFRs at run-time. If the runtime observes overlapping IFRs for conflicting accesses to the same variable in two different threads, it reports a race. The static analysis aggregates information for multiple accesses to the same variable, avoiding the expense of having to instrument every memory access in the program.We directly compare IFRit to FastTrack [10] and Thread-Sanitizer [25], two state-of-the-art fully-precise data-race detectors. We show that IFRit imposes a fraction of the overhead of these detectors. We show that for the PARSEC benchmarks, and several real-world applications, IFRit finds many of the races detected by a fully-precise detector. We also demonstrate that sampling can further reduce IFRit's performance overhead without completely forfeiting precision.

Automatic Detection, Validation, and Repair of Race Conditions in Interrupt-Driven Embedded Software

“…Although interrupts are superficially similar to threads (e.g., nondeterministic execution), the two abstractions have subtle semantic differences [64]. As such, thread-level race detection techniques [10], [20], [37], [50], [54], [58] cannot be adapted to address interrupt-level race conditions.…”

“…Many techniques and algorithms have been proposed to address concurrency faults, such as race conditions. These include static analysis [23], [36], [52], [77], [81], dynamic monitoring [10], [20], [37], [50], schedule exploration [11], [17], [51], [68], [69], [74], test generation [54], [58] and Y. Wang, F. Gao, L. Wang, J. Zhao and X. Li are with State Key Laboratory of Novel Software Technology, Nanjing University, China (email: yuwang cs@nju.edu.cn, fjgao@smail.nju.edu.cn, {lzwang, zhaojh, lxd}@nju.edu.cn). T. Yu is with University of Kentucky, USA (email: tyu@cs.uky.edu).…”

Software Analysis Techniques for Detecting Data Race

“…They usually instrument each load and store of shared memory locations, each call to locking and unlocking calls, and each initialization and allocation of memory. And this causes significant overhead for dynamic tools, where 10× or 100× performance slowdown is not unusual [21], [30], [31]. Hence, there has been a great amount of work to improve the runtime overhead in dynamic techniques.…”

Verification of Data Races in Concurrent Interrupt Handlers

“…Few dynamic race detectors [5, 6] have recently been proposed for detecting data races in interrupt-driven programs. However, legacy race detectors for multithreaded programs [7–9] are very intrusive and cannot directly be applied to interrupt-driven due not only to differences between the programming models, but also to the high overhead they incur. Thus, Lee et al [6] proposed to convert an interrupt-driven program into a corresponding multithreaded program and to use an existing dynamic race detection tool to detect data races into the newly generated program.…”

“…On-the-fly techniques still require less space overhead than postmortem techniques due to the fact that unnecessary information is discarded at runtime as the detection progresses. On-the-fly techniques are generally based either on the happens-before analysis [7, 8, 17, 18], the lockset analysis [19], or the hybrid analysis [9, 20] that is a combination of the first two. However, on-the-fly techniques still suffer from the high runtime overhead they incur.…”