On the Evaluation of Dense Chip-Multiprocessor Architectures
Bibliographic record
Abstract
Chip-multiprocessors (CMPs) have been revealed as the most promising way of making efficient use of current improvements in integration scale. Nowadays, commercial CMP releases integrate at most 8 processor cores onto the chip. However, 16 or more processor cores are expected to be offered in near future dense-CMP (D-CMP) systems. In this way, these architectures impose new design restrictions, and some topics, such as the cache-coherence problem, must be reviewed. In this paper we present an exhaustive performance evaluation of two recently proposed D-CMP architectures, making special emphasis on the solution to the cache-coherence problem that each one of them introduces. The shared bus fabric architecture (SBF) features a snoop cache-coherence protocol and is based on a high-performance bus fabric interconnection network. The second architecture follows a directory-based approach and integrates a bi-dimensional mesh as the interconnection network. Our results show that the performance achieved by the SBF architecture is hard-limited by the bandwidth restrictions of the bus fabric. On the other hand, the directory-based architecture outperforms the SBF one, but presents some performance inefficiencies due to the additional indirection that the directory structure stored in the L2 cache level introduces
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".