Enhancing Static Task Scheduling for Pipelined Cyclic Executions on Heterogeneous Architectures
Bibliographic record
Abstract
Along with the increase in software complexity comes an increasing number of heterogeneous hardware systems, even in real-time safety-critical embedded systems. Programming these heterogeneous systems optimally with respect to resource utilization and overall latency poses a considerable challenge for the developers, and the limited resources in embedded systems only complicate matters. Static scheduling is a promising prospect for maximizing the safety compliance of the final schedule. In earlier work, we have shown that the static scheduling methods found in the literature are capable of calculating near-optimal schedules quickly using a theoretical graph representation of the software. These methods do, however, only schedule a single execution for minimum latency. In environments where the same software is executed continuously, pipelining subsequent executions can often lead to higher throughput. In this paper, we present a novel methodology leveraging known scheduling algorithms to calculate pipelined executions with higher throughput. We propose a model extension that interleaves frames of execution in the same graph in order to utilize parallelism and pipelining effects, and use this extended model to increase the throughput by up to 29.77 %. This method relies on a computationally hard-to-find cut through the graph representation. As such, we further present an approach to reduce the complexity of the problem and approximate the optimum with an error of only 6.3 % in 4.7 % of the runtime.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".