Generating multiprocessor implementations of robotics algorithms from task precedence graphs
Bibliographic record
Abstract
In complex control systems, a single processor is often inadequate to meet real-time deadline demands. A decrease in processing time can be attained by decomposing the algorithm into portions which can run concurrently on a multiprocessor. Implementing an algorithm for a multiprocessor operating system can be arduous and time-consuming; a change to multiprocessor or to the decomposition of the algorithm may require that large portions of the implementation be completely re-written. In this paper we present a utility to graphically model a process or algorithm as a directed, acyclic graph of nodes. The calculations which each node are to perform can then be entered by a programmer. The utility determines a node-to-processor allocation, then combines the node calculations allocated to each processor, and generates all initialisation and inter-node communications code. We present this utility as a tool for investigating multiprocessor multi-tasking algorithms, with application to robotics and control processes. Results of processes which have been so modelled are given.>
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 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".