Automatic Verification of Sequential Consistency for Unbounded Addresses and Data Values ⋆
Bibliographic record
Abstract
Abstract. Sequential consistency is the archetypal correctness condition for thememory protocols of shared-memory multiprocessors. Typically, such protocols are parameterized by the number of processors, the number of addresses, and thenumber of distinguishable data values, and typically, automatic protocol verification analyzes only concrete instances of the protocol with small values (generally < 3) for the protocol parameters. This paper presents a fully automatic method forproving the sequential consistency of an entire parameterized family of protocols, with the number of processors fixed, but the number of addresses and data val-ues being unbounded parameters. Using some practical, reasonable assumptions (data independence, processor symmetry, location symmetry, simple store order-ing, some syntactic restrictions), the method automatically generates a finite-state abstract protocol from the parameterized protocol description; proving sequentialconsistency of the abstract model, via known methods, guarantees sequential consistency of the entire protocol family. The method is sound, but incomplete, butwe argue that it is likely to apply to most real protocols. We present experimental results showing the effectiveness of our method on parameterized versions of thePiranha shared memory protocol and an extended version of a directory protocol from the University of Wisconsin Multifacet Project. 1 Introduction Shared-memory multiprocessors are the dominant form of multiprocessing. In such sys-tems, the processors share a single address space and interact by reading/writing to a shared memory system. A memory model is the correctness condition for the memorysystem, defining the processor-visible behavior of the system.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".