Development of Parallel CFD Applications with the Chapel Programming Language
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-0749.vid Traditionally, Computational Fluid Dynamics (CFD) software uses MPI (Message Passing Interface) to handle the parallelism over distributed memory systems and relies mostly on C, C++ and Fortran to ensure high performance. Consequently, the barrier of entry can be quite high for research and development, and productivity is therefore impacted. The Chapel programming language offers an interesting alternative tailored for research and development of CFD applications. In this paper, the developments of two CFD applications are presented: the first one as an experiment in rewriting a 2D structured flow solver and the second one as writing from scratch a 3D unstructured RANS simulation software named CHAMPS. Details are given on both applications with emphasis on the Chapel features which were used positively in the code design, in particular, to improve flexibility and extend the application from shared memory to distributed memory. Strong and weak scaling is evaluated up to 256 compute nodes on a Cray XC30 for a total of 9216 cores. Finally, CHAMPS is verified against well-established CFD software (FLO82, FUN3D and CFL3D).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".