Existing dynamic race detectors suffer from at least one of the following three limitations: (i) space overhead per memory location grows linearly with the number of parallel threads [13], severely limiting the parallelism that the algorithm can handle; (ii) sequentialization : the parallel program must be processed in a sequential order, usually depth-first [12, 24]. This prevents the analysis from scaling with available hardware parallelism, inherently limiting its performance; (iii) inefficiency : even though race detectors with good theoretical complexity exist, they do not admit efficient implem entations and are unsuitable for practical use [4, 18]. We present a new precise dynamic race detector that leverages structured parallelism in order to address these limitations. Our algorithm requires constant space per memory location, works in parallel, and is efficient in practice. We implemented and evaluated our algorithm on a set of 15 benchmarks. Our experimental results indicate an average (geometric mean) slowdown of 2.78x on a 16-core SMP system.

A common workflow for developing parallel software is as follows: 1) start with a sequential program, 2) identify subcomputations that should be converted to parallel tasks, 3) insert synchronization to achieve the same semantics as the sequential program, and repeat steps 2) and 3) as needed to improve performance. Though this is not the only approach to developing parallel software, it is sufficiently common to warrant special attention as parallel programming becomes ubiquitous. This paper focuses on automating step 3), which is usually the hardest step for developers who lack expertise in parallel programming. Past solutions to the problem of repairing parallel programs have used static-only or dynamic-only approaches, both of which incur significant limitations in practice. Static approaches can guarantee soundness in many cases but are limited in precision when analyzing medium or large-scale software with accesses to pointer-based data structures in multiple procedures. Dynamic approaches are more precise, but their proposed repairs are limited to a single input and are not reflected back in the original source program. In this paper, we introduce a hybrid static+dynamic test-driven approach to repairing data races in structured parallel programs. Our approach includes a novel coupling between static and dynamic analyses. First, we execute the program on a concrete test input and determine the set of data races for this input dynamically. Next, we compute a set of "finish" placements that prevent these races and also respects the static scoping rules of the program while maximizing parallelism. Empirical results on standard benchmarks and student homework submissions from a parallel computing course establish the effectiveness of our approach with respect to compile-time overhead, precision, and performance of the repaired code.