Diagnosing Performance Variations by Comparing Multi-Level Execution Traces
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
Tracing allows the analysis of task interactions with each other and with the operating system. Locating performance problems in a trace is not trivial because of their large size. Furthermore, deep knowledge of all components of the observed system is required to decide whether observed behavior is normal. We introduce TraceCompare, a framework that automatically identifies differences between groups of executions of the same task at the user space and kernel levels. Many performance problems manifest themselves as variations that are easily identified by our framework. Our comparison algorithm takes into account all threads that affect the completion time of analyzed executions. Differences are correlated with application code to facilitate the correction of identified problems. Performance characteristics of task executions are represented by a new data structure called enhanced calling context tree (ECCT). We demonstrate the efficiency of our approach by presenting four case studies in which TraceCompare was used to uncover serious performance problems in enterprise and open source applications, without any prior knowledge of their codebase. We also show that the overhead of our tracing solution is between 0.2 and 9 percent depending on the type of application.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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