An Abstract Semantically Rich Compiler Collocative and Interpretative Model for OpenMP Programs
Bibliographic record
Abstract
To understand the behavior of OpenMP programs, special tools and adaptive techniques are needed for performance analysis. However, these tools provide low-level profile information at the assembly and functions boundaries via instrumentation at the binary or code level, which are very hard to interpret. Moreover, to compare different OpenMP-enabled compilers, there is no systematic methodology that provides an easy comparison. Hence, in this paper, we propose a new model for OpenMP-enabled compilers that assesses the performance differences in well-defined formulations by dividing OpenMP program conditions into four distinct states which account for all the possible cases that an OpenMP program can take. The model works as a first-level inspector to reason about the effect of compiler performance on every state in an unobtrusive and informative way. In addition, an improved version of the standard performance metrics is proposed: speedup, overhead and efficiency based on the model categorization that is state's aware. The evaluation shows that the improved version is more accurate and insightful in terms of OpenMP implementation. Moreover, an algorithmic approach to find patterns between OpenMP compilers is proposed, which is verified along with the model formulations experimentally. We also show the mathematical model behind the optimum performance for any OpenMP program.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| 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".