Memory Consistency Models for Program Transformations: An Intellectual Abstract
Bibliographic record
Abstract
Memory consistency models traditionally specify the behavior of shared memory concurrent hardware. Hardware behavior drifts away from traditional sequential reasoning, thus exhibiting behaviors that are termed as "weak". Weaker consistency models allow for more concurrent behaviors, thus justifying hardware optimizations such as read/write buffers. In parallel, weaker memory models for software allow more compiler optimizations (transformations). However, this "more" may not be strict: certain safe optimizations in stronger models are rendered unsafe in ones weaker than them. We identify properties that must hold among a pair of weak and strong memory models to guarantee this. We propose a framework using which we could build such models, showcasing our results in allowing Read Read reordering over Sequential Consistency (SC). We also show how to partially retain our desired property for a pair of models, placing constraints on the set of transformations or equivalently, on program structure. Lastly, we discuss the potential advantage of designing models satisfying such properties.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".