A dynamic cooperative hybrid MPSO+GA on hybrid CPU+GPU fused multicore
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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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 | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".