RegionScout: Exploiting Coarse Grain Sharing in Snoop-Based Coherence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It has been shown that many requests miss in all remote nodes in shared memory multiprocessors. We are motivated by the observation that this behavior extends to much coarser grain areas of memory. We define a region to be a continuous, aligned memory area whose size is a power of two and observe that many requests find that no other node caches a block in the same region even for regions as large as 16K bytes. We propose RegionScout, a family of simple filter mechanisms that dynamically detect most non-shared regions. A node with a RegionScout filter can determine in advance that a request will miss in all remote nodes. RegionScout filters are implemented as a layered extension over existing snoop-based coherence systems. They require no changes to existing coherence protocols or caches and impose no constraints on what can be cached simultaneously. Their operation is completely transparent to software and the operating system. RegionScout filters require little additional storage and a single additional global signal. These characteristics are made possible by utilizing imprecise information about the regions cached in each node. Since they rely on dynamically collected information RegionScout filters can adapt to changing sharing patterns. We present two applications of RegionScout: In the first RegionScout is used to avoid broadcasts for non-shared regions thus reducing bandwidth. In the second RegionScout is used to avoid snoop induced tag lookups thus reducing energy.
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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 | 0.001 | 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 it