Wormhole: Wisely Predicting Multidimensional Branches
Why this work is in the frame
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Bibliographic record
Abstract
Improving branch prediction accuracy is essential in enabling high-performance processors to find more concurrency and to improve energy efficiency by reducing wrong path instruction execution, a paramount concern in today's power-constrained computing landscape. Branch prediction traditionally considers past branch outcomes as a linear, continuous bit stream through which it searches for patterns and correlations. The state-of-the-art TAGE predictor and its variants follow this approach while varying the length of the global history fragments they consider. This work identifies a construct, inherent to several applications that challenges existing, linear history based branch prediction strategies. It finds that applications have branches that exhibit multi-dimensional correlations. These are branches with the following two attributes: 1) they are enclosed within nested loops, and 2) they exhibit correlation across iterations of the outer loops. Folding the branch history and interpreting it as a multidimensional piece of information, exposes these cross-iteration correlations allowing predictors to search for more complex correlations in the history space with lower cost. We present wormhole, a new side-predictor that exploits these multidimensional histories. Wormhole is integrated alongside ISL-TAGE and leverages information from its existing side-predictors. Experiments show that the wormhole predictor improves accuracy more than existing side-predictors, some of which are commercially available, with a similar hardware cost. Considering 40 diverse application traces, the wormhole predictor reduces MPKI by an average of 2.53% and 3.15% on top of 4KB and 32KB ISL-TAGE predictors respectively. When considering the top four workloads that exhibit multi-dimensional history correlations, Wormhole achieves 22% and 20% MPKI average reductions over 4KB and 32KB ISL-TAGE.
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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