A quantitative analysis of the performance impact of specialized bytecodes in java
Bibliographic record
Abstract
Java is implemented by 201 bytecodes that serve the same purpose as assembler instructions while providing object-file platform independence. A collection of core bytecodes provide critical and independent functionality while a collection of specialized bytecodes is meant to improve on the performance of some of the core bytecodes. This study identifies 67 specialized bytecodes and shows the impact of their removal by despecializing them into semantically equivalent core bytecodes. A detailed analysis of the effects of despecialization on execution efficiency and classfile size was performed. The effects on the SPEC JVM98 Benchmark Suite were analyzed for various subsets of the despecialized bytecodes using three distinct Java virtual machines. When all 67 bytecodes were despecialized, the average slow down across all benchmarks and virtual machines was 2.1 percent, while the single largest performance loss for any one benchmark was 12.7 percent. In some cases, a speedup was observed. An analysis of the impact of despecialization on class file size was also conducted. It was found that the average class file size increased by approximately 6 percent when 67 specialized bytecodes were removed. This study shows that many of the specialized bytecodes currently in use offer little benefit to either execution efficiency or class file size. Thus, they can be considered as candidates for Copyright c ○ 2004 Ben Stephenson and Wade Holst. Permission to copy is hereby granted provided the original copyright notice is reproduced in copies made.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".