Speculative supplier identification for reducing power of interconnects in snoopy cache coherence protocols
Bibliographic record
Abstract
In this work we reduce interconnect power dissipation in Symmetric Multiprocessors or SMPs. We revisit snoopy cache coherence protocols and reduce unnecessary interconnect activity by speculating nodes expected to provide a missing data. Conventional snoopy cache coherence protocols broadcast requests to all nodes, reducing the latency of cache to cache transfer misses at the expense of increasing interconnect power. We show that it is possible to reduce the associated power dissipation if such requests are broadcasted selectively and only to nodes more likely to provide the missing data. We reduce power as we limit access only to the interconnect components between the requester and the supplier node. We evaluate our technique using shared memory applications and show that it is possible to reduce interconnect power by 21% in a 4-way multiprocessor without compromising performance. This comes with negligible hardware overhead.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".