Our troubles with Linux Kernel upgrades and why you should care
Bibliographic record
Abstract
Linux and other open-source Unix variants (and their distributors) provide researchers with full-fledged operating systems that are widely used. However, due to their complexity and rapid development, care should be exercised when using these operating systems for performance experiments, especially in systems research. In particular, the size and continual evolution of the Linux code-base makes it difficult to understand, and as a result, decipher and explain the reasons for performance improvements. In addition, the rapid kernel development cycle means that experimental results can be viewed as out of date, or meaningless, very quickly. We demonstrate that this viewpoint is incorrect because kernel changes can and have introduced both bugs and performance degradations. This paper describes some of our experiences using Linux and FreeBSD as platforms for conducting performance evaluations and some performance regressions we have found. Our results show, these performance regressions can be serious (e.g., repeating identical experiments results in large variability in results) and long lived despite having a large negative effect on performance (one problem was present for more than 3 years). Based on these experiences, we argue: it is sometimes reasonable to use an older kernel version, experimental results need careful analysis to explain why a performance effect occurs, and publishing papers validating prior research is essential.
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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 | 0.030 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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".