An Improved Template-Based JIT Compiler for Java
Bibliographic record
Abstract
Programs that run on a Java virtual machine (JVM)-like Eclipse OpenJ9-are initially interpreted. To improve performance, a Just-in- Time (JIT) compiler may be employed at run time to translate the whole or parts of the program to native code, which can be executed directly instead of being interpreted. In certain environments, where the overhead of an optimizing compiler-the default Testarossa JIT-may be too high to be useful, adding a non-optimizing compiler may prove beneficial to reduce startup times. MicroJIT is such a template-based, lightweight JIT compiler that generates native code with low overhead. This native code runs faster than interpreting the source code, but slower than the optimized code generated by the full JIT compiler. This work proposes improvements to MicroJIT, which include handling polymorphism in a template-based compiler, incorporating exception handling, supporting floating-point types and implementing additional bytecodes. Polymorphism will allow for invocation of virtual methods in Java; exception handling will help maintain the normal desired flow in case of unexpected events; supporting floating-point types will enable compiling methods involving float and double data types; and additional bytecode implementation will increase the bytecode support of MicroJIT. The implementation was tested using the regression framework and evaluated using the DaCapo workloads, most of which depicted increased performance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".