Global Application States Monitoring Applied to Graph Partitioning Optimization
Bibliographic record
Abstract
The paper presents how an advanced graph partitioning optimization method was implemented inside a novel distributed program design framework PEGASUS DA which provides system support for automatic global application states monitoring. In PEGASUS DA, execution control design of distributed applications is system-supported by program global state monitoring run-time. This support provides an automatic construction of user-defined relevant strongly consistent global application states, computing global control predicates on the constructed states, evaluation of these predicates and sending asynchronous control signals to application threads and processes to stimulate the desired global state-driven reactions. The presented graph partitioning optimization algorithm is based on user-defined mixed partitioning strategies. It includes a combined use of different graph partitioning methods and different criteria for definition and assessment of produced partitions. It runs on top of basic graph partitioning methods available inside the METIS partitioning tool. The partitioning is executed by distributed processes and threads controlled by global states monitoring provided by the PEGASUS DA framework. Its use allows easy design and testing of different graph optimization strategies, finding graph partitioning optimal methods and algorithm parameters. The graph partitioning methods presented in the paper are illustrated by experiments performed with partitioning of a number of benchmark graphs to show partitioning quality (from 5% to 30% of the improvement has been observed) and execution time assessment of the proposed approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".