Analyzing blocking to debug performance problems on multi-core systems
Bibliographic record
Abstract
Multi-core systems are rapidly becoming more prevalent. Consequently, developers frequently face performance bugs caused by unexpected interactions between parallel software components. The location of these bugs is difficult to identify with current tools. Indeed, the process exhibiting the slowness may be separated from the root cause of the problem by a blocking chain involving several other processes. This article introduces a new approach for analyzing blocking on multi-core systems and reports on its implementation in the LTTV Delay Analyzer. It enables developers to quickly understand the dependencies among processes and see how the total elapsed time is divided into its main components. The LTTV Delay Analyzer was used to analyze and rapidly correct complex performance problems, something not possible with the existing tools. The Linux Trace Toolkit, LTTng, is used for most of the instrumentation and the trace recording, allowing the tracing of production systems with great accuracy and minimal impact. This approach uses solely kernel instrumentation and does not require the instrumentation or recompilation of processes. The analysis time is linear with respect to trace size.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".