A Dynamic Moldable Job Scheduling Based Parallel SAT Solver
Bibliographic record
Abstract
In a shared cluster facility scheduling of parallel jobs is an important factor that can affect the performance and turnaround time of the submitted jobs. To solve hard tree search problems, we have designed the Dynamic Moldable Tree Search (DMTS) framework. The DMTS framework allows existing serial tree search applications to run in parallel with very little development effort. The DMTS framework is designed to run on parallel computing infrastructures, including clusters, computational grids, and clouds. Target applications for the DMTS framework are hard tree search problems such as Boolean satisfiability (SAT) problems. SAT is amongst the most important problems in theoretical computer science. In this paper we present a parallel Dynamic Moldable SAT (DMSAT) solver. DMSAT runs on top of the DMTS framework. DMSAT is a parallel version of minis at, miniSat is one of the most widely used open source SAT solvers. We compare the performance of DMSAT with PMSAT and miniSat. Our experimental results show that the dynamic moldable model of DMSAT perform much better than the other SAT solvers. In SAT race competitions all those problems that are not solved by a SAT solver within 1200 seconds are marked as unsolvable. DMSAT is also able to solve hard SAT problems that were not solvable by miniSat in the past SAT races.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".