Bibliographic record
Abstract
The task of developing, tuning, and debugging compiler optimizations is a difficult one which can be facilitated by software visualization. There are many characteristics of the code which must be considered when studying the kinds of optimizations which can be performed. Both static data collected at compile-time and dynamic runtime data can reveal opportunities for optimization and affect code transformations. In order to expose the behavior of such complex systems, visualizations should include as much information as possible and accommodate the different sources from which this information is acquired.This paper presents a visualization framework designed to address these issues. The framework is based on a new, extensible language called JIL which provides a common format for encapsulating intermediate representations and associating them with compile-time and runtime data. We present new contributions which extend existing compiler and profiling frameworks, allowing them to export the intermediate languages, analysis results, and code metadata they collect as JIL documents. Visualization interfaces can then combine the JIL data from separate tools, exposing both static and dynamic characteristics of the underlying code. We present such an interface in the form of a new web-based visualizer, allowing JIL documents to be visualized online in a portable, customizable interface.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| 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".