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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".