A Dual Grain Hit-Miss Detector for Large Die-Stacked DRAM Caches
Bibliographic record
Abstract
Die-Stacked DRAM caches offer the promise of improved performance and reduced energy by capturing a larger fraction of an application's working set than on-die SRAM caches. However, given that their latency is only 50% lower than that of main memory, DRAM caches considerably increase latency for misses. They also incur a significant energy overhead for remote lookups in snoop-based multi-socket systems. Ideally, it would be possible to detect in advance that a request will miss in the DRAM cache and thus selectively bypass it. This work proposes a “dual grain filter” which successfully predicts whether an access is a hit or a miss in most cases. Experimental results with commercial and scientific workloads show that a 158KB dual-grain filter can correctly predict data block residency for 85% of all accesses to a 256MB DRAM cache. As a result, average off-die latency with our filter is within 8% of that possible with a perfectly accurate filter, which is impractical to implement.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.006 |
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".