Graphics-Processing-Unit-Based Acceleration of Electromagnetic Transients Simulation
Bibliographic record
Abstract
This paper presents a novel approach to speed up electromagnetic-transients (EMT) simulation, using graphics-processing-unit (GPU)-based computing. This paper extends earlier published works in the area, by exploiting additional parallelism inside EMT simulation. A 2D-parallel matrix-vector multiplication is used that is faster than previous 1D-methods. Also, this paper implements a GPU-specific sparsity technique to further speed up the simulations, as the available CPU-based sparsity techniques are not suitable for GPUs. In addition, as an extension to previous works, this paper demonstrates modelling a power-electronic subsystem. The efficacy of the approach is demonstrated using two different scalable test systems. A low granularity system, that is, one with a large cluster of buses connected to others with a few transmission lines is considered, as is also a high granularity where a small cluster of buses is connected to other clusters, thereby requiring more interconnecting transmission lines. Computation times for GPU-based computing are compared with the computation times for sequential implementations on the CPU. This paper shows two surprising differences of GPU simulation in comparison with CPU simulation. First, the inclusion of sparsity only makes minor reductions in the GPU-based simulation time. Second, excessive granularity, even though it appears to increase the number of parallel-computable subsystems, significantly slows down the GPU-based simulation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".