MultiObjective GPU design space exploration optimization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Obtainable power and performance for GPGPU applications on a GPU depend on many architectural and software parameters. Therefore, it is crucial to have a model to explore the design space and highlight a smaller subset of configurations that meet a given system goal. In this study, we present an application specific, MultiObjective Optimizer that explores the design space of GPUs and finds close to optimum configurations with respect to multiple objectives. The proposed model is composed of three steps to a) find the effective range for configuration parameters, b) predict power and performance of the application by utilizing a Neural Network based predictor and c) analyze the model's predictions and perform Pareto Optimal multiobjective optimization to produce a small subset of configurations which are optimized with respect to both power and performance. We compare the model produced Pareto Optimal configurations to actual Pareto Optimal configurations obtained from simulations and show that the Pareto Optimal configurations obtained from the model is very close to the actual ones.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| 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