A framework for GPU accelerated deformable object modeling
Why this work is in the frame
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Bibliographic record
Abstract
We have developed a framework that uses multicore CPUs and GPUs found on personal computers to accelerate the computations needed for a class of deformable object modeling algorithms. In recent years there has been a growing interest in using deformable objects in computer applications such as animation, video games, garment CAD, and surgical simulation. Deformable object modeling is quite expensive computationally. However, since most of the related calculations can be parallelized, we have developed a framework that utilizes NVIDIA’s CUDA technology to accelerate a set of deformable object modeling algorithms by transferring their core computations to the GPU. Our results show that frame rates can be improved more than 20 times using GPU compared with using a multicore CPU. In addition, we have developed a method called Local Shape Matching which is an extension to the Shape Matching method. Using this new method we have achieved fast and robust simulations whose implementations have good numerical stability.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it