The design and implementation of a modular and extensible Java Virtual Machine
Bibliographic record
Abstract
Abstract This paper describes the design, implementation, and experimental evaluation of a modular and extensible Java™ Virtual Machine (JVM) infrastructure, called Jupiter. The infrastructure is intended to serve as a vehicle for our research on scalable JVM architectures for a cluster of PC workstations, with support for shared memory in software. Jupiter is constructed, using a building block architecture, out of many modules with small, simple interfaces. This flexible structure, similar to UNIX® shells that build complex command pipelines out of discrete programs, allows the rapid prototyping of our research ideas by confining changes in JVM design to a small number of modules. In spite of this flexibility, Jupiter delivers good performance. Experimental evaluation of the current implementation of Jupiter using the SPECjvm98 and the EPCC Java Grande single‐threaded and multithreaded benchmarks reflects competitive performance. Jupiter is on average about 2.5 times faster than Kaffe and about 2 times slower than the Sun Microsystems JDK (interpreter versions only). By providing a flexible JVM infrastructure that delivers competitive performance, we believe we have developed a framework that supports further research into JVM scalability. Copyright © 2003 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 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".