Accelerating GPU-Based Parallel FDTD With Advanced Operator Fusion
Bibliographic record
Abstract
The finite-difference time-domain (FDTD) method is one of the most widely used methods for solving Maxwell's equations, but its efficiency is limited by the Courant–Friedrichs–Lewy (CFL) stability condition. Recent research has extensively explored graphics processing unit (GPU)-based parallel implementations of the FDTD method to enhance computational performance. However, the inherent time-stepping and time-marching nature of the FDTD algorithm leads to frequent kernel launches and low memory efficiency on GPUs, still significantly impacting execution efficiency. This paper proposes a GPU-based FDTD framework enhanced with operator fusion to address this challenge. Within this framework, the FDTD algorithm is represented as a computation graph composed of operators. We classify these operators into different types based on their input and output relationships. Using this type information, a rule-based strategy is developed to merge the operators into larger computational kernels, effectively enhancing GPU execution efficiency. Simulation results demonstrate that the proposed operator fusion framework does not introduce additional errors while achieving a 4× speedup compared to conventional GPU-based implementations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".