CUDA Deformers for Model Reduction
MOTION, INTERACTION AND GAMES (MIG) 2020
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Abstract
Real-time deformable object simulation is important in interactive applications such as games and virtual reality. One common approach to achieve speed is to employ model reduction, a technique whereby the equations of motion of a deformable object are projected to a suitable low-dimensional space. Improving the real-time performance of model-reduced systems has been the subject of much research. While modern GPUs play an important role in real-time simulation and parallel computing, existing model reduction systems typically utilize CPUs and seldom employ GPUs. We give a method to efficiently employ GPUs for vertex position computation in model-reduced simulations. Our CUDA-based algorithm gives a substantial speedup compared to a CPU implementation, thanks to our system architecture that employs a memory layout friendly to GPU memory, reduces the communication between the CPU and GPU, and enables the CPU and GPU to work in parallel.
Comments, questions to Jernej Barbič.
Acknowledgments
- NSF (IIS-1911224)
- USC Annerberg Graduate Fellowships to Bohan Wang
- Bosch Research
- Adobe Research
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