An empirical evaluation of two memory-efficient directory methods
Bibliographic record
Abstract
The authors present an empirical evaluation of two memory-efficient directory methods for maintaining coherent caches in large shared-memory multiprocessors. Both directory methods are modifications of a scheme proposed by L.M. Censier and P. Feautrier (1978) that does not rely on a specific interconnection network and can be readily distributed across interleaved main memory. The schemes considered here overcome the large amount of memory required for tags in the original scheme in two different ways. In the first scheme each main memory block is sectored into sub-blocks for which the large tag overhead is shared. In the second scheme a limited number of large tags are stored in an associative cache and shared among a much larger number of main memory blocks. Simulations show that in terms of access time and network traffic both directory methods provide significant performance improvements over a memory system in which shared-writable data are not cached. The large block sizes required for the sectored scheme, however, promote sufficient false sharing for its performance to be markedly worse than when a tag cache is used.>
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".