Automatic Performance Tuning of Stencil Computations on GPUs
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Bibliographic record
Abstract
We consider automatic performance tuning of stencil computations on Graphics Processing Units. We present a strategy that uses machine learning to determine the best way to use memory followed by a heuristic that divides the remaining optimizations into groups and exhaustively explores one group at a time. We evaluate our strategy using 102 synthetically generated OpenCL stencil kernels on an Nvidia GTX Titan GPU. We assess our strategy both in terms of the number of configurations explored during auto-tuning and the quality of the best configuration obtained. We explore two alternative heuristics that use different groupings of the optimizations. We show that, relative to a random sampling of the space and an expert search, our strategy achieves a reduction in the number of configurations explored of up to 80% and 84% respectively while also finding better performing configurations.
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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