Design of multi-invariant data structures for robust shared accesses in multiprocessor systems
Bibliographic record
Abstract
Multiprocessor systems are widely used in many application programs to enhance system reliability and performance. However, reliability does not come naturally with multiple processors. We develop a multi-invariant data structure approach to ensure efficient and robust access to shared data structures in multiprocessor systems. Essentially, the data structure is designed to satisfy two invariants, a strong invariant, and a weak invariant. The system operates at its peak performance when the strong invariant is true. The system will operate correctly even when only the weak invariant is true, though perhaps at a lower performance level. The design ensures that the weak invariant will always be true in spite of fail-stop processor failures during the execution. By allowing the system to converge to a state satisfying only the weak invariant, the overhead for incorporating fault tolerance can be reduced. We present the basic idea of multi-invariant data structures. We also develop design rules that systematically convert fault-intolerant data abstractions into corresponding fault-tolerant versions. In this transformation, we augment the data structure and access algorithms to ensure that the system always converges to the weak invariant, even in the presence of fail-stop processor failures. We also design methods for the detection of integrity violations and for restoring the strong invariant. Two data structures, namely binary search tree and double-linked list, are used to illustrate the concept of multi-invariant data structures.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".