Design & Implementation of Real-time Parallel GA Operators on the IBM Cell Processor
Why this work is in the frame
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Bibliographic record
Abstract
We present a set of single-core designed parallel SIMD Genetic Algorithm (GA) operators aimed at increasing computational speed of genetic algorithms. We use a discrete-valued chromosome representation. The explored operators include: single gene mutation, uniform crossover and a fitness evaluation function. We discuss their low-level hardware implementations on the Cell Processor. We use the Knapsack problem as a proof of concept, demonstrating performances of our operators. We measure the scalability in terms of generations per second. Using the architecture of the Cell Processor and a static population size of 648 individuals, we achieved 11.6 million generations per second on one Synergetic Processing Element (SPE) core for a problem size n = 8 and 9.5 million generations per second for a problem size n = 16. Generality for a problem size n multiple of 8 is also shown. Executing six independent concurrent GA runs, one per SPE core, allows for a rough overall estimate of 70 million generations per second and 57 million generations per second for problem sizes of n = 8 and n = 16 respectively.
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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