SmartTrack: Efficient Predictive Race Detection

Widely used data race detectors, including the state-of-the-art FastTrack algorithm, incur performance costs that are acceptable for regular in-house testing, but miss races detectable from the analyzed execution. Predictive analyses detect more data races in an analyzed execution than FastTrack detects, but at significantly higher performance cost. This paper presents SmartTrack, an algorithm that optimizes predictive race detection analyses, including two analyses from prior work and a new analysis introduced in this paper. SmartTrack's algorithm incorporates two main optimizations: (1) epoch and ownership optimizations from prior work, applied to predictive analysis for the first time; and (2) novel conflicting critical section optimizations introduced by this paper. Our evaluation shows that SmartTrack achieves performance competitive with FastTrack-a qualitative improvement in the state of the art for data race detection.Comment: Extended arXiv version of PLDI 2020 paper (adds Appendices A-E) #228 SmartTrack: Efficient Predictive Race Detectio

Efficient on-the-fly data race detection in multithreaded C++ programs

Data race detection is essential for debugging multithreaded programs and assuring their correctness. Nevertheless, there is no single universal technique capable of handling the task efficiently, since the data race detection problem is computationally hard in the general case. Thus, to approximate the possible races in a program, all currently available tools take different “short-cuts”, such as using strong assumptions on the program structure or applying various heuristics. When applied to some general case program, however, they usually result in excessive false alarms or in a large number of undetected races. Another major drawback of many currently available tools is that they are restricted, for perfor-mance reasons, to detection units of fixed size. Thus, they all suffer from the same problem—choosing a small unit might result in missing some of the data races, while choosing a large one might lead to false detection. In this work we present a novel testing tool, called MULTIRACE, which combines improved versions of DJIT and LOCKSET—two very powerful on-the-fly algorithms for dynamic detection of apparent data races. Both extended algorithms detect races in multithreaded programs that may execute on weak consistency systems, and may use two-way as well as global synchronization primitives. By employing novel technologies, MULTIRACE adjusts its detection to the native granularity of objects and variables in the program under examination. In order to monitor all accesses to each of the shared locations, MULTIRACE instruments the C++ source code of the program. It lets the user fine-tune the detection process, but otherwise is completely automatic and transparent. This paper describes the algorithms employed in MULTIRACE, as well as its implementation details. The paper also proposes some alternatives to and optimizations of MULTIRACE. It shows that the overheads imposed by MULTIRACE are often much smaller (orders of magnitude) than those obtained by other existing dynamic techniques