Dynamic Analysis of Data Inconsistencies and Data Races in OpenMP Programs
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Yu, Lechen
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Abstract
OpenMP is a popular intra-node parallel programming model that supports several problem decomposition approaches, including task parallelism, data parallelism, and heterogeneous parallelism. When OpenMP introduces new parallel paradigms or features, it must ensure that these additions align with the existing constructs to maintain consistency and avoid unspecified behaviors. New features can result in revisions to the behavior of existing constructs, which may in turn require programmers to re-evaluate their understanding of existing constructs and adapt their code accordingly. Consequently, writing correct OpenMP programs can be challenging even for experienced programmers. To alleviate the intricacy of writing correct OpenMP programs, this thesis outlines several dynamic analysis techniques that help programmers identify programming errors in OpenMP programs. First, we describe various studies on device offloading, a recent OpenMP feature still in its developmental phase. Our studies reveal discrepancies between the LLVM implementation and the intended runtime behavior of device offloading. Additionally, erroneous usage of device offloading constructs can lead to an assortment of memory anomalies. Since these memory anomalies can generate disparities between host variables and their corresponding counterparts on accelerator devices, we classify such bugs as data inconsistencies. By establishing permissible memory accesses on the host and accelerator, this thesis introduces a dynamic approach to detect data inconsistencies automatically. The dynamic approach leverages a per-variable state transition model, which can be used to establish the validity of the memory location before executing any memory accesses. Beyond data inconsistencies, this thesis also delves into novel dynamic approaches for identifying data races in OpenMP programs. By extending the SPD3 race detection algorithm, originally designed for async-finish task parallelism, our dynamic race detection approach can handle a large subset of parallel constructs in OpenMP, thereby checking more precise happens-before relations among tasks relative to existing per-thread vector-clock-based approaches.
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2024-07-25
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Dissertation