Bibliographic record
Abstract
In order to perform meaningful experiments in optimizing compilation and runtime system design, researchers usually rely on a suite of benchmark programs of interest to the optimization technique under consideration. Programs are described as numeric, memory-intensive, concurrent, or object-oriented, based on a qualitative appraisal, in some cases with little justification. In order to make these intuitive notions of program behaviour more concrete and subject to experimental validation, this thesis introduces a methodology to objectively quantify key aspects of program behaviour using dynamic metrics. A set of unambiguous, dynamic, robust and architecture-independent dynamic metrics is defined, and can be used to categorize programs according to their dynamic behaviour in five areas: size, data structures, memory use, polymorphism and concurrency. Each metric is also empirically validated. A general-purpose, easily extensible dynamic analysis framework has been designed and implemented to gather empirical metric results. This framework consists of three major components. The profiling agent collects execution data from a Java virtual machine. The trace analyzer performs computations on this data, and the web interface presents the result of the analysis in a convenient and user-friendly way. The utility of the approach as well as selected specific metrics is validated by examining metric data for a number of commonly used benchmarks. Case studies of program transformations and the consequent effects on metric data are also considered. Results show that the information that can be obtained from the metrics not only corresponds well with the intuitive notions of program behaviour, but can also reveal interesting behaviour that would have otherwise required lengthy investigations using more traditional techniques.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".