Minimizing the Cache Memory Miss Ratio Using Modified Replacement Algorithm (M-CAR)
Bibliographic record
Abstract
Caching is a key method used to close the latency gap between memory and the CPU by using locality in memory accesses.varied cache replacement algorithms have radically varied effects on system performance because they choose which blocks to evict from cache memory in the event of a cache miss.The goal of these replacement strategies is to move closer to the ideal scenario by making the greatest use of the entire cache area, reducing the miss ratio as much as feasible, and obtaining the maximum system performance possible.In this paper, based on clock with adaptive replacement algorithm (CAR), a simple and effective modified algorithm is proposed, namely, modified clock with adaptive replacement (M-CAR), which achieved 91% and 76% hit ratio higher (compared with the conventional CAR method) with datasets that contained 245 and 270 items, respectively.Which is considered to be the most important cache performance criteria.Which means, by default, minimizing the cache miss ratio.As well as the dynamical behavior that has been improved and gained the (M-CAR) that makes it more reliable.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".