TraceSanitizer – Eliminating the Effects of Non-determinism on Error Propagation Analysis
Bibliographic record
Abstract
Modern computing systems typically relax execution determinism, for instance by allowing the CPU scheduler to inter- leave the execution of several threads. While beneficial for perfor- mance, execution non-determinism affects programs’ execution traces and hampers the comparability of repeated executions. We present TraceSanitizer, a novel approach for execution trace com- parison in Error Propagation Analyses (EPA) of multi-threaded programs. TraceSanitizer can identify and compensate for non- determinisms caused either by dynamic memory allocation or by non-deterministic scheduling. We formulate a condition under which TraceSanitizer is guaranteed to achieve a 0% false posi- tive rate and automate its verification using Satisfiability Modulo Theory (SMT) solving techniques. TraceSanitizer is comprehen- sively evaluated using execution traces from the PARSEC and Phoenix benchmarks. In contrast with other approaches, Trace- Sanitizer eliminates false positives without increasing the false negative rate (for a specific class of programs), with reasonable performance overheads.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".