Bibliographic record
Abstract
Traditionally, operating systems use a coarse approximation of memory accesses to implement memory management algorithms by monitoring page faults or scanning page table entries. With finer-grained memory access information, however, the operating system can manage memory muchmore effectively. Previous work has proposed the use of a software mechanism based on virtual page protection and soft faults to track page accesses at finer granularity. In this paper, we show that while this approach is effective for some applications, for many others it results in an unacceptably high overhead. We propose simple Page Access Tracking Hardware (PATH)to provide accurate page access information to the operating system. The suggested hardware support is generic andcan be used by various memory management algorithms. In this paper, we show how the information generated by PATH can be used to implement (i) adaptive page replacement policies, (ii) smart process memory allocation to improve performance or to provide isolation and better process prioritization, and (iii) effectively prefetch virtual memory pages when applications have non-trivial memory access patterns. Our simulation results show that these algorithms can dramatically improve performance (up to 500%) with PATH-provided information, especially when the system is under memory pressure. We show that the software overhead of processing PATH information is less than 6% acrossthe applications we examined (less than 3% in all but two applications), which is at least an order of magni.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.071 | 0.027 |
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".