Discontinuous Galerkin Time Domain Methods in Electromagnetics GPU-Accelerated Numerical Algorithms
Bibliographic record
Abstract
The simulation of electromagnetic (EM) scattering under the excitation of an EM pulse requires high spatial resolution. Our goal is to explore parallel numerical methods to scale for bigger problems. Our process is based on the solution of the first-order discretized Maxwell’s equations in space and time by utilizing the Discontinuous Galerkin (DG) method. This paper presents the theory of modeling a partitioned domain using a DG formulation. A DG with boundary integral equation (BIE) method is used to solve electromagnetic scattering problems. The main motivation of this paper was to investigate the performance of Intel Xeon CPU for large number of degrees-of-freedom (DoFs) and compare these results with an equivalent NVIDIA GPU on Windows Subsystem for Linux (WSL). The purpose is to benchmark scientific computing libraries to see where they are standing performance-wise on CPU compared to a pure GPU approach and the performance impact of virtualization on CPU/GPU computing. The main motivation for using Intel CPU is the built-in AVX2 instructions, and one can use existing generic codes optimized for x86-64 architectures to leverage the utilization of those instructions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".