A constraint programming approach for integrated spatial and temporal scheduling for clustered architectures
Bibliographic record
Abstract
Many embedded processors use clustering to scale up instruction-level parallelism in a cost-effective manner. In a clustered architecture, the registers and functional units are partitioned into smaller units and clusters communicate through register-to-register copy operations. Texas Instruments, for example, has a series of architectures for embedded processors which are clustered. Such an architecture places a heavier burden on the compiler, which must now assign instructions to clusters (spatial scheduling), assign instructions to cycles (temporal scheduling), and schedule copy operations to move data between clusters. We consider instruction scheduling of local blocks of code on clustered architectures to improve performance. Scheduling for space and time is known to be a hard problem. Previous work has proposed greedy approaches based on list scheduling to simultaneously perform spatial and temporal scheduling and phased approaches based on first partitioning a block of code to do spatial assignment and then performing temporal scheduling. Greedy approaches risk making mistakes that are then costly to recover from, and partitioning approaches suffer from the well-known phase ordering problem. In this article, we present a constraint programming approach for scheduling instructions on clustered architectures. We employ a problem decomposition technique that solves spatial and temporal scheduling in an integrated manner. We analyze the effect of different hardware parameters—such as the number of clusters, issue-width, and intercluster communication cost—on application performance. We found that our approach was able to achieve an improvement of up to 26%, on average, over a state-of-the-art technique on superblocks from SPEC 2000 benchmarks.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".