Leveraging High Performance Computing AMD EPYC CPU’s For CFD Applications
Bibliographic record
Abstract
The innovative chiplet-based AMD EPYC™ architecture disrupted the high-performance computing (HPC) market. AMD EPYC processors power Frontier, the world's first exascale supercomputer at Oak Ridge National Laboratory (ORNL). Computational Fluid Dynamics (CFD) applications play a key role in national laboratories, academia, and industry for both scientific discovery and a wide range of real-world simulations, such as aircraft engine design and numerical weather prediction. AMD EPYC processors offer an out-of-box solution for accelerating CFD applications. AMD EPYC processors with AMD 3D V-Cache™ technology have an “X” in the model number and deliver up to three times more L3 Cache per CPU compared to general-purpose AMD EPYC processors with equivalent core counts. AMD tested several standard benchmarks on common CFD applications including OpenFOAM®, WRF® (Weather Research and Forecasting), and Incompact3d that each place unique demands on the CPU. This testing included strong-scaling analysis for up to eight nodes. The results show that AMD EPYC CPUs deliver outstanding performance and scalability running CFD applications. Adding AMD 3D V-Cache further enhances performance and can significantly improve scalability. AMD 3D V-Cache technology can also deliver super-linear scaling in some cases, further reducing time to solution. Faster solutions allow engineers to consider running higher-resolution models in a given timeframe.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".