Race Detection Techniques Research Articles

Optimal prediction of synchronization-preserving races

Concurrent programs are notoriously hard to write correctly, as scheduling nondeterminism introduces subtle errors that are both hard to detect and to reproduce. The most common concurrency errors are (data) races, which occur when memory-conflicting actions are executed concurrently. Consequently, considerable effort has been made towards developing efficient techniques for race detection. The most common approach is dynamic race prediction: given an observed, race-free trace σ of a concurrent program, the task is to decide whether events of σ can be correctly reordered to a trace σ * that witnesses a race hidden in σ. In this work we introduce the notion of sync(hronization)-preserving races. A sync-preserving race occurs in σ when there is a witness σ * in which synchronization operations (e.g., acquisition and release of locks) appear in the same order as in σ. This is a broad definition that strictly subsumes the famous notion of happens-before races. Our main results are as follows. First, we develop a sound and complete algorithm for predicting sync-preserving races. For moderate values of parameters like the number of threads, the algorithm runs in Õ( N ) time and space, where N is the length of the trace σ. Second, we show that the problem has a Ω( N /log 2 N ) space lower bound, and thus our algorithm is essentially time and space optimal. Third, we show that predicting races with even just a single reversal of two sync operations is NP-complete and even W1-hard when parameterized by the number of threads. Thus, sync-preservation characterizes exactly the tractability boundary of race prediction, and our algorithm is nearly optimal for the tractable side. Our experiments show that our algorithm is fast in practice, while sync-preservation characterizes races often missed by state-of-the-art methods.

Efficient noise injection for exposing hidden data races

The happens-before (HB) relation is widely used to precisely detect data races, which are among the major causes of concurrency bugs. However, the HB relation is sensitive to thread interleaving; thus, hidden races that can be revealed in other thread interleavings may remain despite the repeated running of a program with an HB detector. In this paper, we propose a noise injection-based race detection technique (RaceInducer) that can expose and detect hidden races by using the runtime HB analysis result to determine the noise injection instants. RaceInducer delays a thread execution after the occurrence of the first no-lock non-racing accesses of three types (read-exclusive, write-exclusive, and read-shared) to a shared variable during a precise HB detection (FastTrack). Using this technique, RaceInducer can efficiently disturb the normal locking sequences between two threads that generate HB edges hiding data races. We implemented RaceInducer on the RoadRunner instrumentation framework for Java and evaluated its detection capability and runtime overheads on 12 real-world benchmark programs. The evaluation results showed that RaceInducer generated a lower runtime overhead compared to random noise injection that randomly injected noise for no-lock non-racing accesses and can detect more races with the same noise strength. RaceInducer generates particularly lower runtime overhead than the random noise injection for large programs that generates a high number of shared-memory accesses. In addition, we experimentally demonstrated that RaceInducer incurred lower runtime overheads than a current hidden-race detector (DrFinder) for programs with a relatively large number of locking operations.

A Study on Race Condition and Dynamic Data Race Detection Techniques

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

AdaptiveLock: Efficient Hybrid Data Race Detection Based on Real-World Locking Patterns

Among the various types of concurrency bugs, the data race is one of the primary causes of other concurrency bugs. Thus, it is important to detect as many data races as possible during the development step of multithreaded programs. A hybrid data race detection technique that uses the Lockset algorithm and happens-before relation, can detect actually occurred and hidden data races in one execution trace. However, high runtime slowdown obstructs the frequent use of hybrid detectors. In this paper, we empirically demonstrate that most data race bugs are caused by the absence of a lock, and that multiple locks are rarely involved in a data race bug in the real world. Thus, we propose a fast hybrid detection algorithm that does not introduce additional false positives and false negatives to the current hybrid detectors. The suggested algorithm replaces the lock-set intersection by a simple comparison operation that focuses on exploring data-race-prone locking patterns. The experimental results indicate that the proposed algorithm detects the same data races as Multilock-HB, which is the most accurate hybrid detector, with a 1.18\(\times \) slowdown of FastTrack for eight large-scale benchmark programs.

DigHR: precise dynamic detection of hidden races with weak causal relation analysis

Present advanced precise race detection techniques leverage strict causal relation to locate data races, leading to false negatives and missing many hidden races. In this paper, we present a novel race detection technique, called DigHR, which can detect more hidden races than existing precise techniques. DigHR is based on a weak causal relation, called afterward-confirm (AC) relation, which relaxes restrictions of existing causal relations and is proved to be sound. To represent AC relations between events, we propose a new data structure, called water-flow graph, which enable DigHR to analyze AC relations and detect hidden races at run-time. We have implemented DigHR in C/C++ and evaluated the effectiveness of DigHR by detecting races from real-world benchmarks and applications. The experimental results show that DigHR can precisely detect 21% more races than previous techniques, including FastTrack and CP.

Hybrid Data Race Detection for Multicore Software

Multithreaded programs are prone to concurrency errors such as deadlocks, race conditions and atomicity violations. These errors are notoriously difficult to detect due to the non-deterministic nature of concurrent software running on multicore hardware. Data races result from the concurrent access of shared data by multiple threads and can result in unexpected program behaviors. Main dynamic data race detection techniques in the literature are happens-before and lockset algorithms which suffer from high execution time and memory overhead, miss many data races or produce a high number of false alarms. Our goal is to improve the performance of dynamic data race detection, while at the same time improving its accuracy by generating fewer false alarms. We develop a hybrid data race detection algorithm that is a combination of the happens-before and lockset algorithms in a tool. Rather than focusing on individual memory accesses by each thread, we focus on sequence of memory accesses by each thread, called a segment. This allows us to improve the performance of data race detection. We implement several optimizations on our hybrid data race detector and compare our technique with traditional happens-before and lockset detectors. The experiments are performed with C/C++ multithreaded benchmarks using Pthreads library from PARSEC suite and large applications such as Apache web server. Our experiments showed that our hybrid detector is 15 % faster than the happens-before detector and produces 50 % less potential data races than the lockset detector. Ultimately, a hybrid data race detector can improve the performance and accuracy of data race detection, enhancing its usability in practice.

A dynamic predictive race detector for C/C++ programs

Concurrent techniques have been widely adopted in software systems, and data race has become a great threat to stability and security of concurrent systems. Previous precise race detection techniques either may miss many races, or are only suitable for some specific programs, such as the programs executed in a virtual machine rather than in actual hardware. To solves these problems, this paper introduces a dynamic predictive race detector, called LayDetect, which detects predictable races in C/C++ programs. LayDetect applies an innovative layering technique, which can detect more races than other detectors, such as FastTrack. We have implemented and evaluated LayDetect with well-known benchmarks and real-world applications. LayDetect has detected 3.7 M races at run-time which is more than that of FastTrack by two orders of magnitude, while the average slowdown (3.0 $$\times $$ ) and space overhead (34.1 MB) of LayDetect are similar to that of FastTrack.

Software Analysis Techniques for Detecting Data Race

Data races are a multithreading bug. They occur when at least two concurrent threads access a shared variable, and at least one access is a write, and the shared variable is not explicitly protected from simultaneous accesses of the threads. Data races are well-known to be hard to debug, mainly because the effect of the conflicting accesses depends on the interleaving of the thread executions. Hence there has been much effort to detect data races through sophisticated techniques of software analysis by automatically analyzing the behavior of computer programs. Software analysis techniques can be categorized according to the time they are applied: static or dynamic. Static techniques derive program information, such as invariants or program correctness, before runtime from source code, while dynamic techniques examine the behavior at runtime. In this paper, we survey data race detection techniques in each of these two approaches.

Evaluation and comparison of ten data race detection techniques

Many techniques for dynamically detecting data races in multithreaded programs have been proposed. However, it is unclear how these techniques compare in terms of precision, overhead and scalability. This paper presents an experiment to evaluate ten data-race detection techniques on 100 small-scale or middle-scale C/C++ programs. The selected ten techniques, implemented in the same Maple framework, cover not only the classical but also the state-of-the-art in dynamical data-race detection. We compare the ten techniques and try to give reasonable explanations for why some techniques are weaker or stronger than other ones. Evaluation results show that no one technique performs perfectly for all programs according to the three criteria. Based on the evaluation and comparison, we give suggestions of which technique is the most suitable one to use when the target program exhibits particular characteristics. Later researchers can also benefit from our results to construct a better detection technique.

Evaluation and comparison of ten data race detection techniques

Many techniques for dynamically detecting data races in multithreaded programs have been proposed. However, it is unclear how these techniques compare in terms of precision, overhead and scalability. This paper presents an experiment to evaluate ten data-race detection techniques on 100 small-scale or middle-scale C/C++ programs. The selected ten techniques, implemented in the same Maple framework, cover not only the classical but also the state-of-the-art in dynamical data-race detection. We compare the ten techniques and try to give reasonable explanations for why some techniques are weaker or stronger than other ones. Evaluation results show that no one technique performs perfectly for all programs according to the three criteria. Based on the evaluation and comparison, we give suggestions of which technique is the most suitable one to use when the target program exhibits particular characteristics. Later researchers can also benefit from our results to construct a better detection technique.

Effective Techniques for Static Race Detection in Java Parallel Loops

Despite significant progress in recent years, the important problem of static race detection remains open. Previous techniques took a general approach and looked for races by analyzing the effects induced by low-level concurrency constructs (e.g., java.lang.Thread). But constructs and libraries for expressing parallelism at a higher level (e.g., fork-join, futures, parallel loops) are becoming available in all major programming languages. We claim that specializing an analysis to take advantage of the extra semantic information provided by the use of these constructs and libraries improves precision and scalability. We present I te R ace , a set of techniques that are specialized to use the intrinsic thread, safety, and dataflow structure of collections and of the new loop parallelism mechanism introduced in Java 8. Our evaluation shows that I te R ace is fast and precise enough to be practical. It scales to programs of hundreds of thousands of lines of code and reports very few race warnings, thus avoiding a common pitfall of static analyses. In five out of the seven case studies, I te R ace reported no false warnings. Also, it revealed six bugs in real-world applications. We reported four of them: one had already been fixed, and three were new and the developers confirmed and fixed them. Furthermore, we evaluate the effect of each specialization technique on the running time and precision of the analysis. For each application, we run the analysis under 32 different configurations. This allows to analyze each technique's effect both alone and in all possible combinations with other techniques.

Race detection for Android applications

Programming environments for smartphones expose a concurrency model that combines multi-threading and asynchronous event-based dispatch. While this enables the development of efficient and feature-rich applications, unforeseen thread interleavings coupled with non-deterministic reorderings of asynchronous tasks can lead to subtle concurrency errors in the applications. In this paper, we formalize the concurrency semantics of the Android programming model. We further define the happens-before relation for Android applications, and develop a dynamic race detection technique based on this relation. Our relation generalizes the so far independently studied happens-before relations for multi-threaded programs and single-threaded event-driven programs. Additionally, our race detection technique uses a model of the Android runtime environment to reduce false positives. We have implemented a tool called DroidRacer. It generates execution traces by systematically testing Android applications and detects data races by computing the happens-before relation on the traces. We analyzed 15 Android applications including popular applications such as Facebook, Twitter and K-9 Mail. Our results indicate that data races are prevalent in Android applications, and that DroidRacer is an effective tool to identify data races.

Maximal sound predictive race detection with control flow abstraction

Despite the numerous static and dynamic program analysis techniques in the literature, data races remain one of the most common bugs in modern concurrent software. Further, the techniques that do exist either have limited detection capability or are unsound, meaning that they report false positives. We present a sound race detection technique that achieves a provably higher detection capability than existing sound techniques. A key insight of our technique is the inclusion of abstracted control flow information into the execution model, which increases the space of the causal model permitted by classical happens-before or causally-precedes based detectors. By encoding the control flow and a minimal set of feasibility constraints as a group of first-order logic formulae, we formulate race detection as a constraint solving problem. Moreover, we formally prove that our formulation achieves the maximal possible detection capability for any sound dynamic race detector with respect to the same input trace under the sequential consistency memory model. We demonstrate via extensive experimentation that our technique detects more races than the other state-of-the-art sound race detection techniques, and that it is scalable to executions of real world concurrent applications with tens of millions of critical events. These experiments also revealed several previously unknown races in real systems (e.g., Eclipse) that have been confirmed or fixed by the developers. Our tool is also adopted by Eclipse developers.

VORD: A Versatile On-the-fly Race Detection Tool in OpenMP Programs

Shared-memory based parallel programming with OpenMP and Posix-thread APIs becomes more common to fully take advantage of multiprocessor computing environments. One of the critical risks in multithreaded programming is data races that are hard to debug and greatly damaging to parallel applications if they are uncaught. Although ample effort has been made in building specialized data race detection techniques, the state of art tools such as the Intel Thread Checker still have various functionality and performance problems. In this paper, we present a Versatile On-the-fly Race Detection (VORD) tool that provides an agile, efficient, and scalable race detection environment for various parallel programming models. VORD can automatically construct an empirically optimal set of race engines by utilizing classification and adaptation mechanisms. A Race-Detection Classification (RDC) table is created to categorize adequate engines in the aspect of labeling, detecting, and filtering. An Engine Code Property Selector (ECPS) uses the RDC table to adapt optimal engines for the given target programming models. In addition to RDC and ECPS, we have also implemented an OpenMP parser and a source instrumentor. The functionality and efficiency of VORD were compared with those of the Intel Thread Checker by using a set of OpenMP based kernel programs. The experimental results show that VORD can detect data races in more challenging programming models such as nested thread and synchronization models, and can achieve a couple of orders of a magnitude faster processing time than the Intel Thread Checker in the large parallel programs.

병행 Java 프로그램의 확장적 경합탐지를 위한 JDI 기반의 투명한 감시도구

병행 Java 프로그램의 경합은 프로그램의 비결정성을 초래하므로 반드시 탐지되어야 한다. 이러한 경합을 수행 중에 탐지하기 위해서는 스레드에 대한 수행양상과 모든 접근사건들을 감시할 수 있어야 한다. 기존의 경합탐지 기법들은 프로그램의 수행중에 기록된 파일들을 분석하거나 대상 프로그램을 수정하여 감시하므로 스레드나 모든 접근사건들에 대한 감시가 현실적으로 어렵다. 본 연구에서는 JDI(Java Debug Interface)를 이용하여 스레드에 대한 수행양상과 모든 접근사건을 감시하여 확장적 경합탐지를 할 수 있는 투명한 감시도구를 제안한다. 여기서 JDI는 JDPA(Java Platform Debugger Architecture)에서 제공하는 상위 레벨의 100% 순수 자바 인터페이스로써 자바프로그램의 수행중에 특정 정보를 제공할 수 있다. 그리고 제안된 도구의 투명성을 입증하기 위해서 벤치마크 프로그램으로 실험한 결과, 모든 스레드와 접근사건들을 프로그램 수정없이 감시할 수 있었고 프로그램의 감시시간이 20배 이상 증가되었다. Race conditions in current Java programs must be detected because it may cause unexpected result by non-deterministic executions. For detecting such races during program execution, execution flows of all threads and all access events can be monitored. It is difficult for previous race detection techniques to monitor all threads and access events in actuality because these techniques analyze the files traced during program execution or modify original source programs and then monitor these programs. This paper presents a transparent scalable monitoring tool to detect races using JDI(Java Debug Interface) where JDI is 100% pure java interface to provide in JDPA(Java Platform Debugger Architecture) and is able to provide information corresponding to events occurred in run-time of programs. This tool thus can monitor execution flows of all threads and all access events without program modification. We prove transparency of the presented tool and grasp the efficiency of it using a set of published benchmark programs. As a result of this, the suggested tool can monitor all threads and accesses of these programs without their modification, and their monitoring time is increased to more than 20 times.

Race directed random testing of concurrent programs

Bugs in multi-threaded programs often arise due to data races. Numerous static and dynamic program analysis techniques have been proposed to detect data races. We propose a novel randomized dynamic analysis technique that utilizes potential data race information obtained from an existing analysis tool to separate real races from false races without any need for manual inspection. Specifically, we use potential data race information obtained from an existing dynamic analysis technique to control a random scheduler of threads so that real race conditions get created with very high probability and those races get resolved randomly at runtime. Our approach has several advantages over existing dynamic analysis tools. First, we can create a real race condition and resolve the race randomly to see if an error can occur due to the race. Second, we can replay a race revealing execution efficiently by simply using the same seed for random number generation--we do not need to record the execution. Third, our approach has very low overhead compared to other precise dynamic race detection techniques because we only track all synchronization operations and a single pair of memory access statements that are reported to be in a potential race by an existing analysis. We have implemented the technique in a prototype tool for Java and have experimented on a number of large multi-threaded Java programs. We report a number of previously known and unknown bugs and real races in these Java programs.

Hybrid dynamic data race detection

We present a new method for dynamically detecting potential data races in multithreaded programs. Our method improves on the state of the art in accuracy, in usability, and in overhead. We improve accuracy by combining two previously known race detection techniques -- lockset-based detection and happens-before-based detection -- to obtain fewer false positives than lockset-based detection alone. We enhance usability by reporting more information about detected races than any previous dynamic detector. We reduce overhead compared to previous detectors -- particularly for large applications such as Web application servers -- by not relying on happens-before detection alone, by introducing a new optimization to discard redundant information, and by using a "two phase" approach to identify error-prone program points and then focus instrumentation on those points. We justify our claims by presenting the results of applying our tool to a range of Java programs, including the widely-used Web application servers Resin and Apache Tomcat. Our paper also presents a formalization of locksetbased and happens-before-based approaches in a common framework, allowing us to prove a "folk theorem" that happens-before detection reports fewer false positives than lockset-based detection (but can report more false negatives), and to prove that two key optimizations are correct.

Detecting Races with a Production Environment Logic Simulator

A logic simulator serves three functions: computing the output of a circuit board for a given set of input patterns, indicating faults which have been detected by a set of input patterns (tests), and predicting race conditions which may occur. Race detection is important because races prevent faults from being detected and produce unknown states during testing, causing failed boards to pass or good boards to fail. If a simulator fails to detect a race condition, the test engineer does not become aware of its effect until he observes anomalies during production testing. This paper describes a race detection technique which is quite conservative, in that it predicts potential race conditions which are ignored by commonly used simulators. It requires neither modeling of tolerances of components, nor the computation of delays on all parts through the board. Additionally, because the approach is based on a mathematical technique, each logic element state computed by the simulator is available as a dynamic trace printout which helps isolate the source of the race. An example is given in which a race ignored by the unit delay method is detected in a circuit where performance is adversely affected.