Scalable interprocedural register allocation for high level synthesis
Bibliographic record
Abstract
The success of classical high level synthesis has been limited by the complexity of the applications it can handle, typically not large enough to necessitate the departure from the industrial standard, register transfer level design methodology. Recent advances of micro-architecture model enabled the use of stacked based controller, allowing complex algorithms with multiple procedures to be implemented directly in hardware. Nevertheless, design optimizations across procedure boundaries have not been fully explored. In this paper, we address the problem of interprocedural register allocation in the context of high level synthesis. In contrast to a recently proposed interprocedural register allocation algorithm, which processes an expensive, global, graph representation of the conflict relation of all values to achieve near optimally, we introduce a new method, called color palette propagation (CPP). The key idea behind our method, is to propagate the use of colors, whose number is significantly smaller than the size of the conflict relation, across different procedures. With a complexity comparable to intraprocedural register allocation, we show that our method can scale to very large C programs. For those benchmarks that can be handled by conventional global methods, our method produced nearly the same number of registers, while providing an average speedup factor of 90.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".