Parallel Self-Diagnosis of Large Multiprocessor Systems Under the Generalized Comparison Model
Why this work is in the frame
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Bibliographic record
Abstract
This paper deals with the problem of self-diagnosis of multiprocessor and multicomputer systems. We consider the generalized comparison model in which jobs are assigned to pairs of nodes (processors) and the results are compared by the system's nodes themselves (self-diagnosis). The agreements and disagreements among the nodes are the basis for identifying faulty nodes. Genetic algorithms (GAs) have been successfully used for identifying the set of faulty nodes in t-diagnosable systems, where the number of faulty nodes is bounded by t. The major drawback of such a technique is that it is time-consuming specially for large systems. In this paper, we describe a new parallel version of the existing evolutionary diagnosis method, which exploits competing sub-populations to speed up the diagnosis algorithm. Experimental results showed that the new parallel version considerably improved the response time of the diagnosis algorithm, hence, allowing faster identification of faulty nodes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it