FASTDIAGP: An Algorithm for Parallelized Direct Diagnosis
Authors: Viet-Man Le, Cristian Vidal Silva, Alexander Felfernig, David Benavides, José Galindo, Thi Ngoc Trang Tran
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The performance improvements of our proposed algorithm have been shown through empirical results using the Linux-2.6.3.33 configuration knowledge base. |
| Researcher Affiliation | Academia | 1 Graz University of Technology, Graz, Austria 2 Universidad de Talca, Talca, Chile 3 University of Sevilla, Seville, Spain |
| Pseudocode | Yes | Algorithm 1: FASTDIAG(C, B) :, Algorithm 2: FD(C = {c1 . . cn}, B, ρ) : Ω, Algorithm 3: CONSISTENT(C, B, ρ) : Boolean, Algorithm 4: LOOKAHEAD(C, B, φ = {{φ1} . . {φp}}) |
| Open Source Code | Yes | The dataset, the implementation of algorithms, and evaluation programs can be found at https://github.com/AIG-isttugraz/Fast Diag P. |
| Open Datasets | Yes | The basis for these evaluations was the Linux-2.6.33.3 configuration knowledge base taken from Diverso Lab s benchmark1 (Heradio et al. 2022). |
| Dataset Splits | No | The paper does not describe traditional training/test/validation dataset splits as it evaluates a diagnostic algorithm on pre-existing inconsistent sets, rather than training a machine learning model. |
| Hardware Specification | Yes | All experiments reported in the paper were conducted with an Amazon EC2 instance4 of the type c5a.8xlarge, offering 32 v CPUs with 64-GB RAM. |
| Software Dependencies | No | The diagnosis algorithms were implemented in Python using SAT4J (Le Berre and Parrain 2010) as a reasoning solver. We used the CNF class of PYSAT (Ignatiev, Morgado, and Marques-Silva 2018) for representing constraints and the Python multiprocessing package for running parallel tasks. The paper mentions software components like Python, SAT4J, and PYSAT, but does not provide their specific version numbers. |
| Experiment Setup | Yes | In this study, we compared the performance of FASTDIAGP and FASTDIAG according to three aspects: (1) run-time R needed to determine the preferred diagnosis, (2) speedup S that tells us the gain we get through the parallelization, and (3) efficiency E representing the ratio between the speedup and the number of processes in which we run the algorithm. and max GCC = #cores 1 and max GCC = 7. |