Dynamic data replication: an approach to providing fault-tolerant shared memory clusters
Why this work is in the frame
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Bibliographic record
Abstract
A challenging issue in today's server systems is to transparently deal with failures and application-imposed requirements for continuous operation. In this paper we address this problem in shared virtual memory (SVM) clusters at the programming abstraction layer. We design extensions to an existing SVM protocol that has been tuned for low-latency, high-bandwidth interconnects and SMP nodes and we achieve reliability through dynamic replication of application shared data and protocol information. Our extensions allow us to tolerate single (or multiple, but not simultaneous) node failures. We implement our extensions on a state-of-the-art cluster and we evaluate the common, failure-free case. We find that, although the complexity of our protocol is substantially higher than its failure-free counterpart, by taking advantage of architectural features of modern systems our approach imposes low overhead and can be employed for transparently dealing with system failures.
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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 | 0.001 | 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 | 0.002 |
| Open science | 0.003 | 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