FastDiagP: An Algorithm for Parallelized Direct Diagnosis

TL;DR

FastDiagP is a parallelized extension of the FastDiag algorithm that uses speculative programming to improve diagnosis computation speed in large-scale constraint-based systems.

Contribution

We introduce FastDiagP, a novel parallel algorithm that enhances FastDiag's performance through speculative pre-calculations for consistency checks.

Findings

Significant runtime performance improvements demonstrated.

Empirical validation on Linux configuration knowledge base.

Parallelization effectively accelerates diagnosis computation.

Abstract

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.