Predicting SAT Solver Performance on Heterogeneous Hardware
Why this work is in the frame
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Bibliographic record
Abstract
In recent years, a lot of effort has been expended in determining if SAT solver performance is predictable. However, the work in this area invariably focuses on individual machines, and often on individual solvers. It is unclear whether predictions made on a specific solver and machine are accurate when translated to other solvers and hardware. In this work we consider five state-of-the-art solvers, 26 machines and 143 feature instances selected from the 2011 to 2014 SAT competitions. Using combinations of solvers, machines and instances we present four results: First, we show that UNSAT instances are more predictable than corresponding SAT instances. Second, we show that the number of cores in a machine has more impact on performance than L2 cache size. Third, we show that instances with fewer reused clauses are more CPU bound than those where clause reuse is high. Finally, we make accurate predictions of solution time for each of the instances considered across a diverse set of machines.
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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 | 0.000 | 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 | 0.000 | 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