A dynamic cooperative hybrid MPSO+GA on hybrid CPU+GPU fused multicore
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Todays multi-core architectures with accelerators provide tremendous compute power. Population-based metaheuristic algorithms have proven particularly amenable to single instruction multiple data (SIMD)-style parallelization due to the fine-grained parallelism provided by these algorithms. While SIMD hardware allows one to run large scale simulations, obtaining better solution quality often requires a more thoughtful reorganization of the search technique itself. In this paper, we design a hybrid heuristic algorithm that dynamically alternates between Multi-Swarm Particle Swarm Optimization (MPSO) and Genetic Algorithm (GA) to improve solution quality. We parallelize the hybrid algorithm on a hybrid multicore computer, accelerated processing unit (APU) to improve performance. We take advantage of the close coupling the APU provides between CPU and GPU devices. Our hybrid algorithm results indicate an improvement in average solution quality over Multi-Swarm PSO across a set of standard mathematical optimization functions. We study the effect and performance of switching between CPU and GPU devices.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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