Use of Synthetic Benchmarks for Machine-Learning-Based Performance Auto-Tuning
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Bibliographic record
Abstract
We explore the use of synthetic benchmarks for the training phase of machine-learning-based automatic performance tuning. We focus on the problem of predicting if the use of local memory on a GPU is beneficial for caching a single target array in a GPU kernel. We show that the use of only 13 real benchmarks leads to poor prediction accuracy (about to 58%) of the 13 leave-one-out models trained using these benchmarks, even when the model features are sufficiently comprehensive. We define a metric, called the average vicinity density to measure the quality of a training set. We then use it to demonstrate that the poor accuracy of the models built with the real benchmarks is indeed because of the limited size and coverage of the training set. In contrast, the use of 90K properly generated set of synthetic benchmarks leads to significantly better accuracies, up to 87%. These results validate our approach of using synthetic benchmarks for training machine learning models. We describe a synthetic benchmark template for the local memory optimization. We then present two approaches to using this template and a seed set of real benchmarks to generate a large number of synthetic benchmark. We also explore the impact of the number of synthetic benchmarks used in training.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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