GPU Auto-tuning Framework for Optimal Performance and Power Consumption
Bibliographic record
Abstract
An auto-tuning framework for GPU devices is presented for tuning application kernels of OpenCL. The GPU tuner employs multi-objective optimization methodology to improve the performance and power consumption of applications. It efficiently explores a user defined solution space comprising of possible tunable algorithmic and hardware counter variations through code transformations. The methodology targets GPU code tuning situations where performance and energy consumption are critical. The proposed framework is evaluated for 2D convolution kernels. It utilizes a non-dominated sorting Genetic Algorithm with hardware power sensor data for application code transformation through code rewrite and validation. Various algorithmic variations such as loop unrolling, caching, workgroup size and memory utilization are applied. The final pareto optimal configurations code utilized around 30% less power and 4% faster execution time. The analysis shows the convergence of optimization, and 45% improvement in standard deviation.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".