Java TM just-in-time compiler and virtual machine improvements for server and middleware applications
Bibliographic record
Abstract
This paper describes optimization techniques recently applied to the Just-In-Time compilers that are part of the IBM® Developer Kit for JavaTM and the J9 Java virtual machine specification. It focusses primarily on those optimizations that improved server and middleware performance. Large server and middleware applications written in the Java programming language present a variety of performance challenges to virtual machines (VMs) and justin-time (JIT) compilers; we must address not only steady-state performance but also start-up time. In this paper, we describe 12 optimizations that have been implemented in IBM products because they improve the performance and scalability of these types of applications. These optimizations reduce, for example, the overhead of synchronization, object allocation, and some commonly used Java class library calls. We also describe techniques to address server start-up time, such as recompilation strategies. The experimental results show that the optimizations we discuss in this paper improve the performance of applications such as SPECjbb2000 and SPECjAppServer2002 by as much as 10-15%.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".