A Comparative Study of Performance of Eigenvalue Solvers for Parallel Vector Fitting in Multiport Tabulated Data Modeling
Bibliographic record
Abstract
Modelling of high-speed modules such as electronic packages and non-uniform transmission lines based on multiport tabulated measured or EM simulated data is becoming increasingly important in modern designs. Vector Fitting (VF) was first introduced as an algorithm for system identification via rational function approximation from tabulated data. Since the algorithm is iterative in nature, minimizing its computational cost and parallel efficiency on mixed CPU and GPU environments is critical in reducing the overall time needed for convergence. One of the expensive steps in these parallel VF approaches is computing the complex eigenvalues of thousands of small, square matrices that result from the All-Splitting method of the parallel VF algorithm. The computational expense of this step tends to vary vastly based on the solver as well as the multi-core CPU architecture used, hence it is useful to the designer to know which solver and platform to use for efficient use of the algorithm. For this purpose, a comparative performance study of the state-of-the-art eigenvalue solvers when using prominent multi-core platforms of AMD and Intel is presented in the context of parallel VF. Results demonstrate that the architecture as well as the type of solver used can significantly impact the efficiency.
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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 | 0.002 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".