Exploitation of multicore systems in a Java virtual machine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it