A PARALLEL PROBABILISTIC SYSTEM-LEVEL FAULT DIAGNOSIS APPROACH FOR LARGE MULTIPROCESSOR SYSTEMS
Bibliographic record
Abstract
In this paper, we present a system-level fault identification algorithm, using a parallel genetic algorithm, for diagnosing faulty nodes in large heterogeneous systems. The algorithm is based on a probabilistic model where individual node fails with an a priori probability p. The assumptions concerning test outcomes are the same as in the PMC model, that is, fault-free testers always give correct test outcomes and faulty testers are totally unpredictable. The parallel diagnosis algorithm was implemented and simulated on randomly generated large systems. The proposed parallelization is intended to speed up the performance of the evolutionary diagnosis approach, hence reducing the computation time by evolving various sub-populations in parallel. Simulation results are provided showing that the parallel diagnosis did improve the efficiency of the evolutionary diagnosis approach, in that it allowed faster diagnosis of faulty situations, making it a viable alternative to existing techniques of diagnosis. Moreover, the evolutionary approach still provide good results even when extreme non-diagnosable faulty situations are considered.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".