Generating multiprocessor implementations of robotics algorithms from task precedence graphs
Why this work is in the frame
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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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it