Instruction distribution heuristics for quad-cluster, dynamically-scheduled, superscalar processors
Why this work is in the frame
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Bibliographic record
Abstract
We investigate instruction distribution methods for quad-clustec dynamically-scheduled superscalar processors. We study a variety of methods with different cost, performance and complexity characteristics. We investigate both non-adaptive and adaptive methods and their sensitivity both to inter-cluster communication latencies and pipeline depth. Furthermore, we develop a set of models that allow us to identify how well each method attacks issue-bandwidth and inter-cluster communication restrictions. We find that a relatively simple method that changes clusters every other three instructions offers only a 17 % performance slowdown compared to a non-clustered conjguration operating at the same frequency. Moreover; we show that by utilizing adaptive methods it is possible to further reduce this gap down to about 14%. Furthermore, performance appears to be more sensitive to inter-cluster communication latencies rather than to pipeline depth. The best performing method offers a slowdown of about 24 % when inter-cluster communication latency is two cycle. This gap is only 20 % when two additional stages are introduced in the front-end pipeline. 1
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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 | 0.000 | 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 it