Multithreaded programs are notoriously prone to unintended interference between concurrent threads. To address this problem, we argue that yield annotations in the source code should document all thread interference, and we present a type system for verifying the absence of undocumented interference in Java programs. Under this type system, well-typed programs behave as if context switches occur only at yield annotations. Thus, well-typed programs can be understood using intuitive sequential reasoning, except where yield annotations remind the programmer to account for thread interference.

Concurrency bugs are notoriously difficult to find and fix. Several prior empirical studies have identified the prevalence and challenges of concurrency bugs in open source projects, and several existing tools can be used to identify concurrency errors such as data races. However, little is known about how concurrency bugs evolve over time. In this paper, we examine the evolution of data races by analyzing samples of the committed code in two open source projects over a multi-year period. Specifically, we identify how the data races in these programs change over time.

Data races are among the most reliable indicators of programming errors in concurrent software. For at least two decades, Lamport's happens-before (HB) relation has served as the standard test for detecting races--other techniques, such as lockset-based approaches, fail to be sound, as they may falsely warn of races. This work introduces a new relation, causally-precedes (CP), which generalizes happens-before to observe more races without sacrificing soundness. Intuitively, CP tries to capture the concept of happens-before ordered events that must occur in the observed order for the program to observe the same values. What distinguishes CP from past predictive race detection approaches (which also generalize an observed execution to detect races in other plausible executions) is that CP-based race detection is both sound and of polynomial complexity. We demonstrate that the unique aspects of CP result in practical benefit. Applying CP to real-world programs, we successfully analyze server-level applications (e.g., Apache FtpServer) and show that traces longer than in past predictive race analyses can be analyzed in mere seconds to a few minutes. For these programs, CP race detection uncovers races that are hard to detect by repeated execution and HB race detection: a single run of CP race detection produces several races not discovered by 10 separate rounds of happens-before race detection.

Parallel or incremental versions of an algorithm can significantly outperform their counterparts, but are often difficult to develop. Programming models that provide appropriate abstractions to decompose data and tasks can simplify parallelization. We show in this work that the same abstractions can enable both parallel and incremental execution. We present a novel algorithm for parallel self-adjusting computation. This algorithm extends a deterministic parallel programming model (concurrent revisions) with support for recording and repeating computations. On record, we construct a dynamic dependence graph of the parallel computation. On repeat, we reexecute only parts whose dependencies have changed.

We propose a cooperative methodology for multithreaded software, where threads use traditional synchronization idioms such as locks, but additionally document each point of potential thread interference with a "yield" annotation. Under this methodology, code between two successive yield annotations forms a serializable transaction that is amenable to sequential reasoning. This methodology reduces the burden of reasoning about thread interleavings by indicating only those interference points that matter. We present experimental results showing that very few yield annotations are required, typically one or two per thousand lines of code. We also present dynamic analysis algorithms for detecting cooperability violations, where thread interference is not documented by a yield, and for yield annotation inference for legacy software.

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.