TC-GVF: Tensor Core GPU Based Vector Fitting Via Accelerated Tall-Skinny QR Solvers
Why this work is in the frame
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Bibliographic record
Abstract
In this MASc thesis work, the widely used system identification method, Vector Fitting (VF), is advanced for adoption in the emerging GPU architectures with Tensor Cores. Since the VF algorithm is iterative in nature, improving its computational cost and parallel efficiency on mixed CPU and GPU environments is critical in reducing the overall time needed for convergence. Algorithmic advancements are introduced to provide significant speedups to the most computationally expensive steps in the VF process, QR factorization and the solution to a set of linear equations. Furthermore, Nvidia's new Tensor Core architecture is leveraged to provide further performance improvements. The application examples for modeling of large multiport models with high-speed modules demonstrated orders of magnitude speed-up compared to the existing work in the literature.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it