GPU and CPU-Based Parallel FDTD Methods for Frequency-Dependent Transmission Line Models
Bibliographic record
Abstract
Finite-difference time-domain (FDTD) is a popular method utilized for solving frequency-dependent transmission line structures. It is also conveniently applicable to nonuniform wires. The FDTD algorithm discretizes the transmission line problem into a finite number of space-segments and solve for the voltage and current of each segment at every time-step. Therefore, they inherently involve more computations per timestep than conventional terminal based models. In this letter, parallel implementations of a modified FDTD algorithm for a frequency-dependent transmission line problem using multicore CPU and GPU architectures are proposed in order to increase its computational efficiency. Accuracy and performance of the parallel FDTD methods are discussed with examples. The results indicate that a speedup of a few folds compared to serial execution is achieved by the parallel implementation using multicore CPU architecture whereas a massive speedup is achieved by using GPU. The proposed model is also suitable for modelling transmission lines in massively parallel electromagnetic transient (EMT) simulation methods.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".