Dynamic processor allocation for multiple RHC systems in multi-core computing environments
Why this work is in the frame
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Bibliographic record
Abstract
This paper develops a new dynamic processor allocation algorithm for multiple receding horizon controllers (RHC) executing on a multi-core parallel computer. The proposed formulation accounts for bounded model uncertainty, sensor noise, and computation delay. A cost function appropriate for control of multiple coupled vehicle systems on multiple processors is used and an upper bound on the cost as a function of the execution horizon is employed. A parallel processing adaptation of the SNOPT optimization package is used and the efficiency factor of the parallel optimization routine is estimated through simulation benchmarks. Minimization of the cost function upper bound combined with the efficiency factor information results in a combinatorial optimization problem for dynamically allocating the optimal number of logical processors for each RHC subsystem. The new approach is illustrated through simulation of a leader-follower control system for two 3DOF helicopters running on a computer with two quad-core processors.
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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 | 0.000 |
| Open science | 0.000 | 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