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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".