Heterogeneous Accelerated FDTD for Electromagnetic Scattering Problem of Large-Scale Targets
Bibliographic record
Abstract
A large-scale parallel strategy for the finite-difference time-domain (FDTD) method on heterogeneous architectures is presented. A multilevel cooperative parallel framework is developed on heterogeneous accelerator platforms to improve load-balancing efficiency and intersubdomain communication efficiency. An asynchronous remote communication optimization strategy based on temporal updates is proposed to address the high communication intensity of FDTD. With the multilevel cooperative parallel framework, load-balancing efficiency is improved by approximately 40% in weak scaling tests, and a maximum acceleration ratio of about$2\times $can be achieved as the number of nodes increases. With the asynchronous remote communication optimization, the communication overhead is reduced from 69% to 11%, and a maximum acceleration ratio of$2.49\times $is achieved. Finally, the combined optimization strategy is validated through electromagnetic scattering simulations, demonstrating that the algorithm sustains approximately 61% strong-scaling efficiency when scaling from 2800 processes (1.15 million cores) to 28 000 processes (11.5 million cores) on the Tianhe supercomputing platform.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".