Ensuring the correctness of multithreaded programs is difficult, due to the potential for unexpected interactions between concurrent threads. We focus on the fundamental non-interference property of atomicity and present a dynamic analysis for detecting atomicity violations. This analysis combines ideas from both Lipton’s theory of reduction and earlier dynamic race detectors such as Eraser. Experimental results demonstrate that this dynamic atomicity analysis is effective for detecting errors due to unintended interactions between threads. In addition, the majority of methods in our benchmarks are atomic, supporting our hypothesis that atomicity is a standard methodology in multithreaded programming. 1 The Need for Atomicity Multiple threads of control are widely used in software development because they help reduce latency and provide better utilization of multiprocessor machines. However, reasoning about the correctness of multithreaded code is complicated by the nondeterministic interleaving of threads and the potential for unexpected interference between concurrent threads. Since exploring all possible interleavings of the executions of the various threads is clearly impractical, methods for specifying and controlling the interference between concurrent threads are crucial for the development of reliable multithreaded software. Much previous work on controlling thread interference has focused on race conditions, which occur when two threads simultaneously access the same data variable, and at least one of the accesses is a write [1]. Unfortunately, the absence of race conditions is not sufficient to ensure the absence of errors due to unexpected interference between threads. As a concrete illustration of

Abstract We present a novel technique for static race detection in Java pro-grams, comprised of a series of stages that employ a combination of static analyses to successively reduce the pairs of memory accessespotentially involved in a race. We have implemented our technique and applied it to a suite of multi-threaded Java programs. Our ex-periments show that it is precise, scalable, and useful, reporting tens to hundreds of serious and previously unknown concurrency bugsin large, widely-used programs with few false alarms.

This paper presents a novel framework for the symbolic bounds analysis of pointers, array indices, and accessed memory regions. Our framework formulates each analysis problem as a system of inequality constraints between symbolic bound polynomials. It then reduces the constraint system to a linear program. The solution to the linear program provides symbolic lower and upper bounds for the values of pointer and array index variables and for the regions of memory that each statement and procedure accesses. This approach eliminates fundamental problems associated with applying standard xed-point approaches to symbolic analysis problems. Experimental results from our implemented compiler show that the analysis can solve several important problems, including static race detection, automatic parallelization, static detection of array bounds violations, elimination of array bounds checks, and reduction of the number of bits used to store computed values.

Abstract Concurrency bugs are among the most difficult to test and diagnoseof all software bugs. The multicore technology trend worsens this

Multithreaded programs are notoriously prone to race conditions. Prior work on dynamic race detectors includes fast but imprecise race detectors that report false alarms, as well as slow but precise race detectors that never report false alarms. The latter typically use expensive vector clock operations that require time linear in the number of program threads. This paper exploits the insight that the full generality of vector clocks is unnecessary in most cases. That is, we can replace heavyweight vector clocks with an adaptive lightweight representation that, for almost all operations of the target program, requires only constant space and supports constant-time operations. This representation change significantly improves time and space performance, with no loss in precision. Experimental results on Java benchmarks including the Eclipse development environment show that our FASTTRACK race detector is an order of magnitude faster than a traditional vector-clock race detector, and roughly twice as fast as the high-performance DJIT + algorithm. FASTTRACK is even comparable in speed to ERASER on our Java benchmarks, while never reporting false alarms.

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Atomicity is a fundamental correctness property in multithreaded programs, both because atomic code blocks are amenable to sequential reasoning (which significantly simplifies correctness arguments), and because atomicity violations often reveal defects in a program’s synchronization structure. Unfortunately, all atomicity analyses developed to date are incomplete in that they may yield false alarms on correctly synchronized programs, which limits their usefulness. We present the first dynamic analysis for atomicity that is both sound and complete. The analysis reasons about the exact dependencies between operations in the observed trace of the target program, and it reports error messages if and only if the observed trace is not conflict-serializable. Despite this significant increase in precision, the performance and coverage of our analysis is competitive with earlier incomplete dynamic analyses for atomicity.

Data races often result in unexpected and erroneous behavior. In addition to causing data corruption and leading programs to crash, the presence of data races complicates the semantics of an execution which might no longer be sequentially consistent. Motivated by these observations, we have designed and implemented a Java runtime system that monitors program executions and throws a Data-RaceException when a data race is about to occur. Analogous to other runtime exceptions, the DataRaceException provides two key benefits. First, accesses causing race conditions are interrupted and handled before they cause errors that may be difficult to diagnose later. Second, if no DataRaceException is thrown in an execution, it is guaranteed to be sequentially consistent. This strong guarantee helps to rule out many concurrency-related possibilities as the cause of erroneous behavior. When a DataRaceException is caught, the operation, thread, or program causing it can be terminated gracefully. Alternatively, the DataRaceException can serve as a conflict-detection mechanism in optimistic uses of concurrency. We start with the definition of data-race-free executions in the Java memory model. We generalize this definition to executions that use transactions in addition to locks and volatile variables for synchronization. We present a precise and efficient algorithm for dynamically verifying that an execution is free of data races. This algorithm generalizes the Goldilocks algorithm for data-race detection by handling transactions and providing the ability to distinguish between read and write accesses. We have implemented our algorithm and the DataRaceException in the Kaffe Java Virtual Machine. We have evaluated our system on a variety of publicly available Java benchmarks and a few microbenchmarks that combine lock-based and transaction-based synchronization. Our experiments indicate that our implementation has reasonable overhead. Therefore, we believe that in addition to being a debugging tool, the DataRaceException may be a viable mechanism to enforce the safety of executions of multithreaded Java programs. Categories and Subject Descriptors D.2.4 [Software Engineering]: Software/Program Verification — formal methods, reliability,

Many concurrency bugs in multi-threaded programs are due to data races. There have been many efforts to develop static and dynamic mechanisms to automatically find the data races. Most of the prior work has focused on finding the data races and eliminating the false positives. In this paper, we instead focus on a dynamic analysis technique to automatically classify the data races into two categories – the data races that are potentially benign and the data races that are potentially harmful. A harmful data race is a real bug that needs to be fixed. This classification is needed to focus the triaging effort on those data races that are potentially harmful. Without prioritizing the data races we have found that there are too many data races to triage. Our second focus is to automatically provide to the developer a reproducible scenario of the data race, which allows the developer to understand the different effects of a harmful data race on a program’s execution. To achieve the above, we record a multi-threaded program’s execution in a replay log. The replay log is used to replay the multithreaded program, and during replay we find the data races using a happens-before based algorithm. To automatically classify if a data race that we find is potentially benign or potentially harmful, we replay the execution twice for a given data race – one for each possible order between the conflicting memory operations. If the two replays for the two orders produce the same result, then we classify the data race to be potentially benign. We discuss our experiences in using our replay based dynamic data race checker on several Microsoft applications.

Abstract. Multithreaded programs are prone to errors caused by unintended interference between concurrent threads. This paper focuses on verifying that deterministically-parallel code is free of such thread interference errors. Deterministically-parallel code may create and use new threads, via fork and join, and coordinate their behavior with synchronization primitives, such as barriers and semaphores. Such code does not satisfy the traditional non-interference property of atomicity (or serializability), however, and so existing atomicity tools are inadequate for checking deterministically-parallel code. We introduce a new non-interference specification for deterministically-parallel code, and we present a dynamic analysis to enforce it. We also describe SingleTrack, a prototype implementation of this analysis. SingleTrack’s performance is competitive with prior atomicity checkers, but it produces many fewer spurious warnings because it enforces a more general noninterference property that is applicable to more software. 1