Learning Heuristics for the Superblock Instruction Scheduling Problem
Why this work is in the frame
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Bibliographic record
Abstract
Modern processors have multiple pipelined functional units and can issue more than one instruction per clock cycle. This places a burden on the compiler to schedule the instructions to take maximum advantage of the underlying hardware. Superblocks - a straight-line sequence of code with a single entry point and multiple possible exit points - are a commonly used scheduling region within compilers. Superblock scheduling is NP-complete, and is done suboptimally in production compilers using a greedy algorithm coupled with a heuristic. The heuristic is usually handcrafted, a potentially time-consuming process. In this paper, we show that supervised machine learning techniques can be used to semiautomate the construction of heuristics for superblock scheduling. In our approach, labeled training data were produced using an optimal superblock scheduler. A decision tree learning algorithm was then used to induce a heuristic from the training data. The automatically constructed decision tree heuristic was compared against the best previously proposed, handcrafted heuristics for superblock scheduling on the SPEC 2000 and MediaBench benchmark suites. On these benchmark suites, the decision tree heuristic reduced the number of superblocks that were not optimally scheduled by up to 38 percent, and led to improved performance on some architectural models and competitive performance on others.
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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