Analyzing blocking to debug performance problems on multi-core systems
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
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.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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