Internal Clock Drift Estimation in Computer Clusters
Bibliographic record
Abstract
Most computers have several high‐resolution timing sources, from the programmable interrupt timer to the cycle counter. Yet, even at a precision of one cycle in ten millions, clocks may drift significantly in a single second at a clock frequency of several GHz. When tracing the low‐level system events in computer clusters, such as packet sending or reception, each computer system records its own events using an internal clock. In order to properly understand the global system behavior and performance, as reported by the events recorded on each computer, it is important to estimate precisely the clock differences and drift between the different computers in the system. This article studies the clock precision and stability of several computer systems, with different architectures. It also studies the typical network delay characteristics, since time synchronization algorithms rely on the exchange of network packets and are dependent on the symmetry of the delays. A very precise clock, based on the atomic time provided by the GPS satellite network, was used as a reference to measure clock drifts and network delays. The results obtained are of immediate use to all applications which depend on computer clocks or network time synchronization accuracy.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".