Catenation and operand specialization for Tcl virtual machine performance
Bibliographic record
Abstract
We present techniques for eliminating dispatch overhead in a virtual machine interpreter using a lightweight just-in-time native-code compilation. In the context of the Tcl VM, we convert bytecodes to native code, by concatenating the native instructions used by the VM to implement each bytecode instruction. We thus eliminate the dispatch loop. Furthermore, immediate arguments of bytecode instructions are substituted into the native code using run-time specialization. Native code output from C compiler is not amenable to relocation by copying; fix-up of the code is required for correct execution. Resulting code size increase is apparently impractical. We evaluate performance using hardware performance counters and system simulation. Some benchmarks achieve up to 50% speedup, but roughly half slow down, or exhibit little change. Most slowdown is attributable to I-cache overflow due to increased code size, and increased compilation time. Larger I-caches broaden applicability of technique.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".