UNIVERSITY OF CALGARY Floey, an Intermediate Language for Optimizing Compilers
Bibliographic record
Abstract
In modern optimizing compilers, linear human-readable text representation of a program is first transformed into an abstract syntax tree that represents the structure of that program. Abstract syntax tree is then transformed into intermediate representation (IR), based on which compiler optimizations are accomplished. The optimized IR is sent to the code generator and finally translated into assembly or machine code. Research on IRs has been focused on how they can be designed to facilitate compiler optimizations or more effective code generation on specific architecture. This thesis presents a mid-level intermediate language, called Floey. In a Floey program, control flowgraphs are separated into different tree-like structures called control expressions. Different control expressions are connected by entries. On Floey, a machine independent optimization, called the reduction algorithm, is implemented. By comparing the reduction algorithm to various conventional optimizations, we argue that not only Floey facilities compiler optimization design, it also provides a cleaner and uniform perspective on compiler optimizations in general.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".