TASTyTruffle: Just-in-Time Specialization of Parametric Polymorphism
Bibliographic record
Abstract
Parametric polymorphism enables programmers to express algorithms independently of the types of values that they operate on. The approach used to implement parametric polymorphism can have important performance implications. One popular approach, erasure, uses a uniform representation for generic data, which entails primitive boxing and other indirections that harm performance. Erasure destroys type information that could be used by language implementations to optimize generic code. We present TASTyTruffle, an implementation for a subset of the Scala programming language. Instead of JVM bytecode, TASTyTruffle interprets Scala's TASTy intermediate representation, a typed representation wherein generic types are not erased. TASTy's precise type information empowers TASTyTruffle to implement generic code more effectively. In particular, it allows TASTyTruffle to reify types as run-time objects that can be passed around. Using reified types, TASTyTruffle supports heterogeneous box-free representations for generic values. TASTyTruffle also uses reified types to specialize generic code, producing monomorphic copies of generic code that can be easily and reliably optimized by its just-in-time (JIT) compiler. Empirically, TASTyTruffle is competitive with standard JVM implementations on a small set of benchmark programs; when generic code is used with multiple types, TASTyTruffle consistently outperforms the JVM. The precise type information in TASTy enables TASTyTruffle to find additional optimization opportunities that could not be uncovered with erased JVM bytecode.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".