GPU accelerated Paired Explicit Runge-Kutta methods for high-orderspatial discretizations
Bibliographic record
Abstract
Abstract: The ability to perform unsteady scale-resolving simulations, such as Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS), relies on accurate, ef?cient, 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 ?ow 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 signi?cant 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 ?nal presentation.
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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 | 0.001 |
| 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 | 0.000 | 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".