GPU accelerated Paired Explicit Runge-Kutta methods for high-orderspatial discretizations
Bibliographic record
Abstract
The ability to perform unsteady scale-resolving simulations, such as Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS), relies on accurate, efficient, and stable discretizations that are synergistic with modern high-performance computing architectures.In this paper we explore a combination of Graphical Processing Units (GPUs) combined with Paired Explicit Runge-Kutta (P-ERK) temporal discretization for high-order accurate LES/DNS solvers.The P-ERK approach is a fully explicit solver technology that allows different Runge-Kutta schemes with different numbers of active stages to be using in stiff and non-stiff regions of the domain.Results from LES of turbulent flow over an SD7003 airfoil demonstrate that speedup factors of 17.76 and 6.05 can be obtained from GPU acceleration and P-ERK, separately.Combining these yields speedup factors up to 112.This represents a significant two order of magnitude reduction in the computational cost of performing LES/DNS.Final qualitative and quantitative results will be provided for a range of test cases in the final presentation.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".