A Fixed-Structure Learning Automaton Solution to the Stochastic Static Mapping Problem
Bibliographic record
Abstract
This paper considers the problem of distributing the processes of a parallel application onto a set of computing nodes. This problem called the static mapping problem (SMP) is known to be NP-hard, and has been tackled using heuristic solutions. The objective of this paper is to present the first reported learning automaton (LA) based solution to the SMP, generated by the close resemblance of the SMP to the equipartitioning problem. The LA in question is of the so-called fixed-structure family, solution to the equipartitioning problem is then modified to solve the SMP. Several algorithmic variants of this solution have been implemented, and these have all been rigorously tested and evaluated through extensive simulations on randomly generated parallel applications. The focus in this work is to demonstrate the applicability of LA to the SMP, not to optimise and evaluate the performance of the proposed strategy. The results presented here clearly demonstrate that LA provides a promising tool that can effectively solve the mapping problem.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".