Constraint-based applications attempt to identify a solution that meets all
defined user requirements. If the requirements are inconsistent with the
underlying constraint set, algorithms that compute diagnoses for inconsistent
constraints should be implemented to help users resolve the "no solution could
be found" dilemma. FastDiag is a typical direct diagnosis algorithm that
supports diagnosis calculation without predetermining conflicts. However, this
approach faces runtime performance issues, especially when analyzing complex
and large-scale knowledge bases. In this paper, we propose a novel algorithm,
so-called FastDiagP, which is based on the idea of speculative programming.
This algorithm extends FastDiag by integrating a parallelization mechanism that
anticipates and pre-calculates consistency checks requested by FastDiag. This
mechanism helps to provide consistency checks with fast answers and boosts the
algorithm's runtime performance. The performance improvements of our proposed
algorithm have been shown through empirical results using the Linux-2.6.3.33
configuration knowledge base.

本研究提出了一个基于猜测编程理论的 FastDiagP 并行化诊断算法来改善 FastDiag 算法的性能问题，该算法通过预测需求提前执行一部分计算来提高性能，并在 Linux-2.6.3.33 配置知识库中得到了实证结果验证。