CUDA Deformers for Model Reduction
CUDA Deformers for Model Reduction
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DOI：
10.1145/3424636.3426895
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发表时间：
2020-10
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影响因子：
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通讯作者：
Bohan Wang;J. Barbič
中科院分区：
文献类型：
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作者：
Bohan Wang;J. Barbič
实时可变形物体仿真在游戏、虚拟现实等交互式应用中具有重要意义。实现速度的一种常见方法是采用模型简化，这是一种将可变形物体的运动方程投影到合适的低维空间的技术。提高模型简化系统的实时性能一直是许多研究的主题。虽然现代GPU在实时仿真和并行计算中发挥着重要作用，但现有的模型简化系统通常使用CPU，很少使用GPU。我们给出了一种方法，有效地利用GPU的顶点位置计算模型简化模拟。与CPU实现相比，我们基于CUDA的算法提供了大幅加速，这要归功于我们的系统架构，该架构采用了对GPU内存友好的内存布局，减少了CPU和GPU之间的通信，并使CPU和GPU能够并行工作。
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.