Revisiting hardware-assisted page walks for virtualized systems
Bibliographic record
Abstract
Recent improvements in architectural supports for virtualization have extended traditional hardware page walkers to traverse nested page tables. However, current two-dimensional (2D) page walkers have been designed under the assumption that the usage patterns of guest and nested page tables are similar. In this paper, we revisit the architectural supports for nested page table walks to incorporate the unique characteristics of memory management by hypervisors. Unlike page tables in native systems, nested page table sizes do not impose significant overheads on the overall memory usage. Based on this observation, we propose to use flat nested page tables to reduce unnecessary memory references for nested walks. A competing mechanism to HW 2D page walkers is shadow paging, which duplicates guest page tables but provides direct translations from guest virtual to system physical addresses. However, shadow paging has been suffering from the overheads of synchronization between guest and shadow page tables. The second mechanism we propose is a speculative shadow paging mechanism, called speculative inverted shadow paging, which is backed by non-speculative flat nested page tables. The speculative mechanism provides a direct translation with a single memory reference for common cases, and eliminates the page table synchronization overheads. We evaluate the proposed schemes with the real Xen hypervisor running on a full system simulator. The flat page tables improve a state-of-the-art 2D page walker with a page walk cache and nested TLB by 7%. The speculative shadow paging improves the same 2D page walker by 14%.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".