N-Lane Bridge Performance Antipattern Analysis Using System-Level Execution Tracing
Bibliographic record
Abstract
Performance problems caused by the improper use of multi-threading can be incredibly difficult to diagnose. There are countless resources that could introduce latency into an application when multiple cooperating threads interact improperly. As a matter of program comprehension, it is crucial to know which resources are being misused by the program causing that program to run slower. The concept of performance antipatterns has been introduced in order to classify common performance problems and bundle them with a solution. The One Lane Bridge (OLB) antipattern in particular deals with latency due to the incorrect use of multi-threading. However, existing methods to detect the OLB antipattern do not consider latency caused by active resources and use imprecise metrics. In this paper, we present a new category of OLB, the N-Lane Bridge antipattern, to cover situations of latency caused by the overuse of active resources. Moreover, a novel system-level execution tracing approach is presented to detect both categories of OLB antipatterns. As a proof-of-concept, we applied our approach to the popular Firefox web browser application and we were able to identify several OLB antipatterns, enabling us to diagnose and understand a critical performance issue.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".