TC-GVF: Tensor Core GPU-Based Vector Fitting via Accelerated Tall-Skinny QR Solvers
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Bibliographic record
Abstract
QR decomposition and solution of linear least-squares-based large system of equations form the backbone of computational flow in many scientific applications. Usually, these account for the bulk of the computational cost in these applications, such as in vector fitting (VF) methods, which are widely used for system identification via rational function approximation from tabulated data of high-speed modules. Since the VF algorithm is iterative in nature, minimizing its computational cost and increasing its parallel efficiency on mixed CPU and GPU environments are critical in reducing the time needed for each iteration. In this article, a novel tensor core-based QR (TC-QR) decomposition method and tensor core-based linear least-squares-based solver (TC-LLS) are introduced to speed up the computationally expensive steps of QR factorization and solution to a set of linear least-squares equations, exploiting the emerging GPU platforms with tensor core (TC) architectures. These modules are utilized in developing the TC GPU-based VF (TC-GVF) algorithm, providing significant speedup compared with the state-of-the-art GVF implementations 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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