Optimal Split Point Placement for Predictable GPU Wavefront Splitting
Why this work is in the frame
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Bibliographic record
Abstract
Predictable wavefront splitting (PWS) is an optimization technique for graphics processing units (GPUs) to address the performance and worst-case execution time (WCET) impacts of branch divergence. PWS relies on manual annotation by the GPU programmer; these choices affect the resulting WCET. This work automates this process with two key approaches. First, we formulate the optimal annotation as an integer quadratic programming (IQP) problem such that the solution guarantees the lowest WCET. Second, we show that the problem can be solved with an optimal polynomial-time dynamic programming algorithm that achieves the same solutions as the IQP. We implement our algorithm in a compiler flow for an AMD GPU, and we deploy the annotated executable on a gem5 micro-architectural implementation of the AMD GCN3 GPU. We evaluate our implementation on a benchmark suite provided by AMD and supplement it with an extensive set of synthetic benchmarks. Our evaluation shows that these two approaches are able to reduce the WCET by between 13% and 31% compared to five baseline algorithms.
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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 | 0.001 | 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