A Framework 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 techniques for coherence traffic reduction and prefetching suggest that further useful patterns emerge with a macroscopic, coarse-grain view. To exploit coarse- grain behavior, previous work extended conventional caches with additional coarse-grain tracking and management structures considerably increasing overall cost and complexity. This paper demonstrates that as multi-megabyte caches have become commonplace, coarse-grain tracking and management no longer needs to be an afterthought. This functionality comes "for free" via RegionTracker. RegionTracker is a dual-grain cache design that maintains block-level communication while directly supporting coarse-grain tracking and management. Compared to a block-centric conventional cache of the same data capacity, RegionTracker requires less area to achieve a nearly identical miss rate (within 1%). RegionTracker 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, commercial workloads, and area estimates based on full-custom layouts on a 130 nm commercial technology, we demonstrate the performance and cost viability of the RegionTracker design. We also demonstrate the potential of RegionTracker as a framework for coarse-grain optimizations by showing that it 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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.011 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".