Bibliographic record
Abstract
Miss rate curves (MRCs) are useful in a number of contexts. In our research, online L2 cache MRCs enable us to dynamically identify optimal cache sizes when cache-partitioning a shared-cache multicore processor. Obtaining L2 MRCs has generally been assumed to be expensive when done in software and consequently, their usage for online optimizations has been limited. To address these problems and opportunities, we have developed a low-overhead software technique to obtain L2 MRCs online on current processors, exploiting features available in their performance monitoring units so that no changes to the application source code or binaries are required. Our technique, called RapidMRC, requires a single probing period of roughly 221 million processor cycles (147 ms), and subsequently 124 million cycles (83 ms) to process the data. We demonstrate its accuracy by comparing the obtained MRCs to the actual L2 MRCs of 30 applications taken from SPECcpu2006, SPECcpu2000, and SPECjbb2000. We show that RapidMRC can be applied to sizing cache partitions, helping to achieve performance improvements of up to 27%.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".