Register pressure aware scheduling for high level synthesis
Bibliographic record
Abstract
Variations of list scheduling became the de-facto standard of scheduling straight line code in software compilers, a trend faithfully inherited by high-level synthesis solutions. Due to its nature, list scheduling is oblivious of the tightly coupled register pressure; a dangling fundamental problem that has been attacked by the compiler community for decades, and which results, in case of highlevel synthesis, in excessive instantiations of registers and accompanying steering logic. To alleviate this problem, we propose a synthesis framework called soft scheduling, which acts as a resource unconstrained pre-scheduling stage that restricts subsequent scheduling to minimize register pressure. This optimization objective is formulated as a live range minimization problem, a measure shown to be proportional to register pressure, and optimally solved in polynomial time using minimum cost network flow formulation. Unlike past solutions in the compiler community, which try to reduce register pressure by local serialization of subject instructions, the proposed solution operates on the entire basic block or hyperblock and systematically handles instruction chaining subject to the same objective. The application of the proposed solution to a set of real-life benchmarks results in a register pressure reduction ranging, on average, between 11% and 41% depending on the compilation and synthesis configurations with minor 2% to 4% increase in schedule latency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".