Bibliographic record
Abstract
Checkpoint prediction and intelligent management have been recently proposed for reducing the number of coarse-grain checkpoints needed to achieve high performance through speculative execution. In this work, we take a closer look at various checkpoint prediction and management alternatives, comparing their performance and requirements as the scheduler window size increases. We also study a few additional design choices. The key contribution of this work is BranchTap, a novel checkpoint-aware speculation strategy that temporarily throttles speculation to reduce recovery cost while allowing speculation to proceed when it is likely to boost performance. BranchTap dynamically adapts to application behavior. We demonstrate that for a 1K-entry window processor with a FIFO of just four checkpoints, our adaptive speculation control mechanism leads to an average performance degradation of just 1.49% compared to a processor that has an infinite number of checkpoints. This represents an improvement of 28.3% over using just prediction-based checkpoint allocation. Average performance degradation without BranchTap is 2.08%. For the same configuration, BranchTap decreases the worst case deterioration from 8.99% to 5.64%.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".