We present OMPRACER, a static tool that uses flow-sensitive, interprocedural analysis to detect data races in OpenMP programs. OMPRACER is fast, scalable, has high code coverage, and supports the most common OpenMP features by combining state-of-the-art pointer analysis, novel value-flow analysis, happens-before tracking, and generalized modelling of OpenMP APIs. Unlike dynamic tools that currently dominate data race detection, OMPRACER achieves almost 100% code coverage using static analysis to detect a broader category of races without running the program or relying on specific input or runtime behaviour. OMPRACER has competitive precision with dynamic tools like Archer and ROMP: passing 105/116 cases in DataRaceBench with a total accuracy of 91%. OMPRACER has been used to analyze several Exascale Computing Project proxy applications containing over 2 million lines of code in under 10 minutes. OMPRACER has revealed previously unknown races in an ECP proxy app and a production simulation for COVID19.

OMPRacer: A Scalable and Precise Static Race Detector for OpenMP Programs

DataRaceBench is a dedicated benchmark suite to evaluate tools aimed to find data race bugs in OpenMP programs. Since its initial release in 2017, DataRaceBench has been widely used by tool developers to find the strengths and limitations of their tools. The results also provide an apple-to-apple comparison of the state-of-the-art of data race detection tools. In this paper, we discuss our latest efforts to enhance DataRaceBench. In particular, we have added support for Fortran language and some of the newest OpenMP 5.0 language features. We have also added new kernels representing new patterns from literature and other benchmarks (e.g., NAS Parallel Benchmark). To reduce duplicated code patterns in the benchmark suite, we have designed a distance-based code similarity analysis, combining both static and dynamic code features. Finally, we dockerize tools and streamline the entire benchmarking process to quickly generate a dashboard showing the state-of-the-art of data race detection of OpenMP programs. The enhanced DataRaceBench is released as v 1.3.0, with 222 newly added benchmarks. 56 of them are in C, and the remaining 166 are in Fortran, reproducing the C programs' nature. Our experiments show that this new version can spot more limitations of the current data race detection tools, with significantly reduced user efforts needed to run experiments.

Enhancing DataRaceBench for Evaluating Data Race Detection Tools

Program parallelization is a common software engineering task, in which parallel design patterns are applied. While the focus of parallelization is on performance, the functional behavior should be kept invariant, i.e., sequential and parallelized program should be functionally equivalent. Several verification techniques exist that analyze properties of parallel programs, but only a few approaches inspect functional equivalence between a sequential program and its parallelization. Even fewer approaches consider parallel design patterns when checking equivalence.

PatEC: Pattern-Based Equivalence Checking

OpenMP is a popular API for the development of parallel, shared memory programs and allows programmers to easily ready their programs to utilize modern multi-core processors. However, OpenMP-compliant programs do not guarantee that the OpenMP parallelization is functionally equivalent to a sequential execution of the program. Therefore, several approaches analyze OpenMP programs. While some approaches check functional equivalence, they are either general purpose approaches, which ignore the structure of the program and the design pattern applied for parallelization, or they focus on parallelized for-loops. In this paper, we propose a verification approach that aims at pipeline parallelism. To show functional equivalence, our approach mainly computes the dependencies that a sequential execution imposes on the pipeline stages and checks whether these dependencies are incorporated in the OpenMP parallelzation. We implemented our verification approach in a prototype tool and evaluated it on some examples. Our evaluation shows that our approach soundly detects incorrect pipeline implementations.

Verifying Pipeline Implementations in OpenMP

Data races are a primary source of concurrency bugs in parallel programs. Yet, debugging data races is not easy, even with a large amount of data race detection tools. In particular, there still exists a manually-intensive and time-consuming investigation process after data races are reported by existing race detection tools. To address this issue, we present OMPSanitizer in this paper. OMPSanitizer employs a novel and semantic-aware impact analysis mechanism to assess the potential impact of detected data races so that developers can focus on data races with a high probability to produce a harmful impact. This way, OMPSanitizer can remove the heavy debugging burden of data races from developers and simultaneously enhance the debugging efficiency. We have implemented OMPSanitizer based on the widely-used dynamic binary instrumentation infrastructure, Intel Pin. Our evaluation results on a broad range of OpenMP programs from the DataRaceBench benchmark suite and an ECP Proxy application demonstrate that OMPSanitizer can precisely report the impact of data races detected by existing race detectors, e.g., Helgrind and ThreadSanitizer. We believe OMPSanitizer will provide a new perspective on automating the debugging support for data races in OpenMP programs.

Does it matter?: OMPSanitizer: an impact analyzer of reported data races in OpenMP programs

In the era of Exascale computing, writing efficient parallel programs is indispensable, and, at the same time, writing sound parallel programs is very difficult. Specifying parallelism with frameworks such as OpenMP is relatively easy, but data races in these programs are an important source of bugs. In this article, we propose LLOV, a fast, lightweight, language agnostic, and static data race checker for OpenMP programs based on the LLVM compiler framework. We compare LLOV with other state-of-the-art data race checkers on a variety of well-established benchmarks. We show that the precision, accuracy, and the F1 score of LLOV is comparable to other checkers while being orders of magnitude faster. To the best of our knowledge, LLOV is the only tool among the state-of-the-art data race checkers that can verify a C/C++ or FORTRAN program to be data race free.

LLOV: A Fast Static Data-Race Checker for OpenMP Programs

In the era of Exascale computing, writing efficient parallel programs is indispensable and at the same time, writing sound parallel programs is very difficult. Specifying parallelism with frameworks such as OpenMP is relatively easy, but data races in these programs are an important source of bugs. In this paper, we propose LLOV, a fast, lightweight, language agnostic, and static data race checker for OpenMP programs based on the LLVM compiler framework. We compare LLOV with other state-of-the-art data race checkers on a variety of well-established benchmarks. We show that the precision, accuracy, and the F1 score of LLOV is comparable to other checkers while being orders of magnitude faster. To the best of our knowledge, LLOV is the only tool among the state-of-the-art data race checkers that can verify a C/C++ or FORTRAN program to be data race free.

Pankaj Kukreja

Papers

LLOV: A Fast Static Data-Race Checker for OpenMP Programs

LLOV: A Fast Static Data-Race Checker for OpenMP Programs