Automatic speculative parallelization of loops using polyhedral dependence analysis
Why this work is in the frame
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Bibliographic record
Abstract
Speculative Execution (SE) runs loops in parallel even in the presence of a dependence. Using polyhedral dependence analysis, more speculation candidate loops can be discovered than normal OpenMP parallelization. In this research, a framework is implemented that can automatically perform speculative parallelization of loops using Polly's [15] polyhedral dependence analysis. The framework uses two different heuristics to find speculation candidates. The first heuristic allows loops with only may dependences to run speculatively in parallel while the second heuristic filters out cold loops and, using profile information, loops with actual run time dependences. The framework is fully automatic. Running SPEC2006 and the PolyBench/C benchmarks on the IBM BlueGene/Q [16] machine shows that the framework is able to discover more parallelization candidates than OpenMP parallelization and achieve better speedup.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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