Workload Reduction for Multi-input Feedback-Directed Optimization
Bibliographic record
Abstract
Feedback-directed optimization is an effective technique to improve program performance, but it may result in program performance and compiler behavior that is sensitive to both the selection of inputs used for training and the actual input in each run of the program. Cross-validation over a workload of inputs can address the input-sensitivity problem, but introduces the need to select a representative workload of minimal size from the population of available inputs. We present a compiler-centric clustering methodology to group similar inputs so that redundant inputs can be eliminated from the training workload. Input similarity is determined based on the compile-time code transformations made by the compiler after training separately on each input. Differences between inputs are weighted by a performance metric based on cross-validation in order to account for code transformation differences that have little impact on performance. We introduce the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CrossError</i> metric that allows the exploration of correlations between transformations based on the results of clustering. The methodology is applied to several SPEC benchmark programs, and illustrated using selected case studies.
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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".