TC-GVF: Tensor Core GPU Based Vector Fitting Via Accelerated Tall-Skinny QR Solvers
Bibliographic record
Abstract
With the continually increasing demand for high-frequency operations and low-power consumption, signal and power integrity issues are emerging as the major bottlenecks in high-speed designs.Predicting these issues requires efficient and accurate modeling of high-speed modules such as uniform and non-uniform transmission lines, multiport packages, electromagnetic modules and tabulated data measured modules.Vector fitting (VF) was first introduced as an algorithm for system identification via rational function approximation from tabulated multiport data.Since the 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 the convergence.Previous work in the literature explored how parallel VF's performance was affected by the distribution of the algorithm's workload across each CPU and GPU core.In this thesis, 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.List of Figures 2.1 CPU vs GPU architectures: GPUs devote more transistors to compute data processing [14]. . . . . .
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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 | 0.002 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".