Evaluating the Performance of the Eclipse OpenJ9 JVM JIT Compiler on AArch64
Bibliographic record
Abstract
The embedded computing market, which includes Internet-of-Things (IoT) and mobile computing devices, is a non-traditional computing market where computation resources are limited. Therefore, software, particularly the managed runtime, is required to be more compact and efficient than in a cloud/desktop-based environment. This paper focuses on porting the Eclipse OpenJ9 runtime, a Java Virtual Machine (JVM), built on top of Eclipse OMR, to a new environment while continuing to provide a generic runtime environment. The low-power AArch64 (ARMv8-A) platform is becoming the answer for resource constrained environments of embedded systems. We evaluate and validate the AArch64 implementation of OpenJ9’s Just-in-Time (JIT) compiler against more mature architectures currently available, namely x86-64. The evaluation reveals performance discrepancies and necessary improvements, beyond those that are already known. Our work is an effort to template new architectural support and allow others to follow our model. We provide a baseline for future research on OpenJ9, OMR and the JIT on the AArch64 platform and outline some improvements as future work.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| 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".