gpuMF: a framework for parallel hybrid metaheuristics on GPU with application to the minimisation of harmonics in multilevel inverters
Bibliographic record
Abstract
Metaheuristics are non-deterministic optimisation algorithms used to solve complex problems for which classic approaches are unsuitable or unable to generate satisfying solutions in a reasonable time. Despite their effectiveness, metaheuristics require considerable computational power. Multiple efforts have been made on the development of parallel metaheuristics on graphics processing units (GPUs). Based on a massively parallel architecture, the GPU offers remarkable computing power and can provide significant speedup. However, there currently exists no software project that unites these research initiatives into a comprehensive and reusable tool. To address this shortcoming, we developed gpuMF, a framework for parallel hybrid metaheuristics on GPUs. GPU metaheuristic framework (gpuMF) exploits the intrinsic parallelism found in metaheuristics and fully utilises the massively parallel architecture of GPUs. To demonstrate the effectiveness of our framework, we use gpuMF to minimise the harmonics of multilevel inverters while providing a speedup of 276x.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".