Analyzing the performance of code-copying virtual machines
Bibliographic record
Abstract
Many popular programming languages use interpreter-based execution for portability, supporting dynamic or reflective properties, and ease of implementation. Code-copying is an optimization technique for interpreters that reduces the performance gap between interpretation and JIT compilation, offering significant speedups over direct-threading interpretation. Due to varying language features and virtual machine design, however, not all languages benefit from codecopying to the same extent. We consider here properties of interpreted languages, and in particular bytecode and virtual machine construction that enhance or reduce the impact of code-copying. We implemented code-copying and compared performance with the original direct-threading virtual machines for three languages, Java (SableVM), OCaml, and Ruby (Yarv), examining performance on three different architectures, ia32 (Pentium 4), x86_64 (AMD64) and PowerPC (G5). Best speedups are achieved on ia32 by OCaml (maximum 4.88 times, 2.81 times on average), where a small and simple bytecode design facilitates improvements to branch prediction brought by code-copying. Yarv only slightly improves over direct-threading; large working sizes of bytecodes, and a relatively small fraction of time spent in the actual interpreter loop both limit the application of codecopying and its overall net effect. We are able to show that simple ahead of time analysis of VM and execution properties can help determine the suitability of code-copying for a particular VM before an implementation of code-copying is even attempted.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".