String deduplication during garbage collection in virtual machines
Bibliographic record
Abstract
Memory management is a significant topic in virtual machine research. As allocation and deallocation of objects is performed automatically, garbage collection (GC) has become an important field of research. It aims to speed up and optimize the execution of applications developed in languages such as Java, C#, Python and others. Even though GC techniques have become more sophisticated, automatic memory management is not optimal. Garbage collection techniques, such as reference counting, mark-sweep, mark-compact, copying collection and generational garbage collection build the base of most automated memory management environments. Most GC policies include a stop-the-world phase that is used to detect live objects.The research presented in this paper aims to improve the automatic memory management and application execution by investigating an optimization of the memory layout. The goal of the approach described is to utilize the stop-the-world phase of the garbage collector in order to detect duplicate strings and to deduplicate them before copying them to a different region. The goal of this algorithm is to reduce memory duplication, as well as copying of memory, in order to decrease the heap size and therefore the number of garbage collections required to execute the client application.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".