New strategies for developing GPU accelerated disk‐based discontinuous deformation analysis for large‐scale modeling
Bibliographic record
Abstract
Abstract The major obstacle for the application of discontinuous deformation analysis (DDA) in engineering problems is the high computational cost and poor efficiency. In this paper, the main algorithms of disk‐based DDA are redesigned and implemented on graphics processing unit (GPU) to improve its performance. First, a contact pair‐wise scheme is proposed to assemble the stiffness matrix on GPU. Second, a buffer strategy and a GPU version of grid‐based contact detection algorithm are developed to improve the efficiency of contact detection. Third, for solving the simultaneous equations, two iterative methods are considered along with the direct solver method. The parallel performances of proposed strategies are tested and compared with the CPU counterparts. The results show that the maximum speedup ratio is 14 for the assembly of the stiffness matrix and 215 for contact detection. The speedup ratio for solving simultaneous equations depends on several factors, and the preconditioned conjugate gradients method (pcg) is suggested. Finally, the effectiveness and performance of the proposed GPU accelerated disk‐based DDA is further demonstrated by several examples, one of which consisted of over 500,000 particles. The results show that the proposed method can achieve a satisfactory speedup ratio, and is ready for large‐scale problems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".