Pattern Recognition Techniques Applied to the Abstraction of Traces of Inter-Process Communication
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Bibliographic record
Abstract
The large number of processors in high performance computing and distributed applications is becoming a major challenge in the analysis of the way an application's processes communicate with each other. In this paper, we propose an approach that facilitates the understanding of large traces of inter-process communication by extracting communication patterns that characterize their main behavior. Two algorithms are proposed. The first one permits the recognition of repeating patterns in traces of MPI (Message Passing Interface) applications whereas the second algorithm searches if a given communication pattern occurs in a trace. Both algorithms are based on the n-gram extraction technique used in natural language processing. Unlike existing work, our approach operates on the trace as it is generated (i.e. on the fly) and does not require complex and computationally-expensive data structures. We show the effectiveness and efficiency of our approach in detecting communication patterns from large traces generated from two target systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it