A Building Block for Coarse-Grain Optimizations in the On-Chip Memory Hierarchy
Bibliographic record
Abstract
Current on-chip block-centric memory hierarchies exploit access patterns at the fine-grain scale of small blocks. Several recently proposed memory hierarchy enhancements for coherence traffic reduction and prefetching suggest that additional useful patterns emerge with a macroscopic, coarse-grain view. This paper presents RegionTracker, a dual-grain, on-chip cache design that exposes coarse-grain behavior while maintaining block-level communication. RegionTracker eliminates the extraneous, often imprecise coarse-grain tracking structures of previous proposals. It can be used as the building block for coarse-grain optimizations, reducing their overall cost and easing their adoption. Using full-system simulation of a quad-core chip multiprocessor and commercial workloads, we demonstrate that RegionTracker overcomes the inefficiencies of previous coarse-grain cache designs. We also demonstrate how RegionTracker boosts the benefits and reduces the cost of a previously proposed snoop reduction 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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".