Java Runtime Optimization for Copying Arrays on AArch64
Bibliographic record
Abstract
One of the architectures supported by OpenJ9, the AArch64 platform, is widely used in electronic devices because of its reasonable price and resource efficiency. This work adds an optimization in the Just-In-Time (JIT) compiler of OpenJ9 for AArch64, that copies arrays efficiently. The optimizing JIT compiler function, arraycopy Evaluator, separates inlinable code for better performance. Making use of Vector Floating Point registers helps in copying up to 128 bits of any data type in a single load/store instruction. While copying, the situations where primitive values are copied or Garbage Collection checks are required to access the reference fields are handled. We evaluate the results using the BumbleBench Microbenchmarking test framework. We investigate the trace files and utilize the Perf tool to identify the causes of unexpected Benchmark results. We achieve an up to tenfold increase in performance.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".