Increasing TLB reach by exploiting clustering in page translations
Bibliographic record
Abstract
The steadily increasing sizes of main memory capacities require corresponding increases in the processor's translation lookaside buffer (TLB) resources to avoid performance bottlenecks. Large operating system page sizes can mitigate the bottleneck with a smaller TLB, but most OSs and applications do not fully utilize the large-page support in current hardware. Recent work has shown that, while not guaranteed, some virtual-to-physical page mappings exhibit “contiguous” spatial locality in which consecutive virtual pages map to consecutive physical pages. Such locality provides opportunities to coalesce “adjacent” TLB entries for increased reach. We observe that beyond simple adjacent-entry coalescing, many more translations exhibit “clustered” spatial locality in which a group or cluster of nearby virtual pages map to a similarly clustered set of physical pages. In this work, we provide a detailed characterization of the spatial locality among the virtual-to-physical translations. Based on this characterization, we present a multi-granular TLB organization that significantly increases its effective reach and reduces miss rates substantially while requiring no additional OS support. Our evaluation shows that the multi-granular design outperforms conventional TLBs and the recently proposed coalesced TLBs technique.
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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 | 0.002 |
| 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) | 0.003 | 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".