A multi‐granularity locking scheme for java packedobjects based on a concurrent multiway tree
Bibliographic record
Abstract
Summary In this paper, we develop a multi‐granularity locking scheme for Java PackedObjects, an experimental enhancement introduced in IBM's J9 Java Virtual Machine. The packed object model organizes data in a multi‐tier manner in which object data can be nested in the container object instead of being pointed to by an object reference, as in the traditional Java object model. This new object data model creates new challenges for multi‐tier data synchronization, requiring concurrent locks on the multi‐tier data of different granularities for maintaining consistency. This is different from the traditional Java synchronization model. In this paper we make use of a concurrent multiway tree to represent the containing and ordering relationship between PackedObjects at different tiers and develop an efficient multi‐granularity locking scheme allowing multiple threads to concurrently manipulate the concurrent multiway tree for synchronization operations. In the evaluation, we compare our new tree‐based multitierSync with the previous multitierSync approaches based on linked‐lists (optimized‐list‐based and lazy‐list‐based). The experimental results show that the tree‐based MultitierPackedSync outperforms the list‐based approaches considerably in different workloads, and the higher the workload, the better the performance gains achieved by the tree‐based MultitierPackedSync.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".