Scalability Study on Large-Scale Parallel Finite Element Computing in PANDA Frame
Bibliographic record
Abstract
A Finite-element parallel computing frame—PANDA and its implementation processes are introduced. To validate the parallel performance of the PANDA frame, a series of tests were carried out to obtain the computing scale and the speedup ratios. First, three different large-scale freedom degree models (i.e. 1.83 million, 7 million and 10 million) of a typical engineering clamp were created in MSC.Patran and were translated into geometric-grid files that can be identified in PANDA frame. Second, Linear static parallel computations of the three cases were successfully carried out on large parallel computers with preconditioned conjugate gradient methods in PANDA frame. The speedup ratios of the three cases were obtained with a maximum process number of 64. The results show that the PANDA frame is competent for carrying out large-scale parallel computing of 10 million freedom degrees. In each scale，the parallel computing is nearly linearly accelerated along with the increase of process numbers, moreover, a super-linear speedup appears in some cases. The speedup curves show that the linear degree increases when the computing scale enlarges. The influence of different communication bandwidths on computing efficiency was also discussed. All the testing results indicate that the PANDA frame has excellent parallel performance and favorable computing scalability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".