Parallel back-propagation neural network training technique using CUDA on multiple GPUs
Bibliographic record
Abstract
A parallel Back-Propagation(BP) neural network training technique using Compute Unified Device Architecture (CUDA) on multiple Graphics Processing Units(GPUs) is proposed. To exploit the maximum performance of GPUs, we propose to implement batch mode BP training by building input neurons, hidden neurons and output neurons into matrix form. The implementation includes CUDA Basic Linear Algebra Subroutines (cuBLAS) function to perform matrix and vector operations and CUDA kernel. The proposed technique utilizes multiple GPUs to achieve further acceleration. Each GPU has the same neural network structure and weight parameter. The number of training samples are distributed to multiple GPUs. Each GPU calculates local training error and the gradient at each layer then transferred to the first GPU to calculate the summations. The summations are transferred back to each GPU to update the local weights until the training goal is achieved. A cavity microwave bandpass filter example is used to illustrate the validity of this technique.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".