Teaching old caches new tricks: RegionTracker and predictor virtualization
Bibliographic record
Abstract
On-chip last-level caches are increasing to tens of megabytes to accommodate applications with large memory footprints and to compensate for high memory latencies and limited off-chip bandwidth. This paper reviews two on-going research efforts that exploit such large caches: coarse-grain cache management, and predictor virtualization. Coarse-grain cache management collects and stores cache information at a large memory region granularity (e.g., 1 KB to 8 KB). This coarse view of memory access behaviour enables optimizations that were not previously possible with conventional caches. Predictor virtualization is motivated by the observation that on-chip storage has become sufficiently large to accommodate allocating, on demand, a small percentage of its capacity for purposes other than storing program data and instructions. Predictor virtualization uses conventional caches to store program metadata, i.e., information about program behaviour. Such metadata information can be used for several optimizations that improve performance and power. This paper summarizes the progress made and the on-going activity in these two research efforts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| 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".