DProf: distributed profiler with strong guarantees
Bibliographic record
Abstract
Performance analysis of a distributed system is typically achieved by collecting profiles whose underlying events are timestamped with unsynchronized clocks of multiple machines in the system. To allow comparison of timestamps taken at different machines, several timestamp synchronization algorithms have been developed. However, the inaccuracies associated with these algorithms can lead to inaccuracies in the final results of performance analysis. To address this problem, in this paper, we develop a system for constructing distributed performance profiles called DProf. At the core of DProf is a new timestamp synchronization algorithm, FreeZer, that tightly bounds the inaccuracy in a converted timestamp to a time interval. This not only allows timestamps from different machines to be compared, it also enables maintaining strong guarantees throughout the comparison which can be carefully transformed into guarantees for analysis results. To demonstrate the utility of DProf, we use it to implement dCSP and dCOZ that are accuracy bounded distributed versions of Context Sensitive Profiles and Causal Profiles developed for shared memory systems. While dCSP enables user to ascertain existence of a performance bottleneck, dCOZ estimates the expected performance benefit from eliminating that bottleneck. Experiments with three distributed applications on a cluster of heterogeneous machines validate that inferences via dCSP and dCOZ are highly accurate. Moreover, if FreeZer is replaced by two existing timestamp algorithms (linear regression & convex hull), the inferences provided by dCSP and dCOZ are severely degraded.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".