Exploitation of multicore systems in a Java virtual machine
Bibliographic record
Abstract
The Java® programming language and the Java virtual machine (JVM®) are intended to provide a level of abstraction from the underlying hardware and operating system (OS). This abstraction poses challenges from a performance perspective, because developers are often unable to make use of best-practice approaches during software development for their deployed OSs and hardware platforms. The rise of multicore processor systems has been swift and has changed many software developers' underlying assumptions with respect to the hardware over the last ten years. The role of the JVM is to hide such platform complexity by adapting appropriately through runtime analysis and reaction to application behavior. The JVM is an essential component for exploiting the full potential of multicore processor systems through effective management of the memory subsystem, removing impediments to application and system scalability with respect to the number of logical processors, producing efficient and highly optimized code, and providing user tools for monitoring and analysis. This paper reviews the key techniques and tools available in the IBM Developer Kit for the Java 6 release for managing and optimizing Java for multicore processor environments and describes performance results to demonstrate the effectiveness of such tools and techniques.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 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".