Interprocedural Specialization of Higher-Order Dynamic Languages Without Static Analysis
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Bibliographic record
Abstract
Function duplication is widely used by JIT compilers to efficiently implement dynamic languages. When the source language supports higher order functions, the called function's identity is not generally known when compiling a call site, thus limiting the use of function duplication. This paper presents a JIT compilation technique enabling function duplication in the presence of higher order functions. Unlike existing techniques, our approach uses dynamic dispatch at call sites instead of relying on a conservative analysis to discover function identity. We have implemented the technique in a JIT compiler for Scheme. Experiments show that it is efficient at removing type checks, allowing the removal of almost all the run time type checks for several benchmarks. This allows the compiler to generate code up to 50% faster. We show that the technique can be used to duplicate functions using other run time information opening up new applications such as register allocation based duplication and aggressive inlining.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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