Optimum off-line trace synchronization of computer clusters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A tracing and monitoring framework produces detailed execution trace files for a system. Each trace file contains events with associated timestamps based on the local clock of their respective system, which are not perfectly synchronized. To monitor all behavior in multi-core distributed systems, a global time reference is required, thus the need for traces synchronization techniques. The synchronization is time consuming when there is a cluster of many computers. In this paper we propose an optimized technique to reduce the total synchronization time. Compared with related techniques that have been used on kernel level traces, this method improves the performance while maintaining a high accuracy. It uses the packet rate and the hop count as two major criteria to focus the computation on more accurate network links during synchronization. These criteria, tested in real-word experiments, were identified as most important features of a network. Furthermore, we present numerical and analytical evaluation results, and compare these with previous methods demonstrating the accuracy and the performance of the method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Open science | 0.001 | 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