Bibliographic record
Abstract
This paper describes an extension of the TNE algorithm, the objective of which is to increase its parallelism and to break the inter-processor deadlock inherent with the use of TNE. The algorithm, which we call the SGTNE algorithm (Semi Global TNE), is executed over a cluster of processors as opposed to TNE, which is executed over a cluster of processes assigned to a single processor. SGTNE helps to break the inter-processor deadlock by executing a shortest path algorithm over a snapshot of the LPs in a cluster of processors. This paper discusses the algorithm and its implementation and reports on the performance results of simulations of a partitioned FCFS queueing network model executed on the Intel Paragon A4 multiprocessor machine. We also examine the impact of partitioning on the efficient implementation of the SGTNE algorithm. The results obtained indicate that SGTNE yields good speedups and that a partitioning which makes use of a strongly connected component algorithm results in a reduction of 30% in the running time of a simulation when compared to simple partitioning strategies. The results also indicate that SGTNE outperforms TNE.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".