Parallel 2D mesh smoothing using GPU computing.

摘要

We present new efficient parallel approaches for 2D mesh smoothing on a GPU for Zhou and Shimada's [12] and Xu and Newman's [13] 2D mesh smoothing algorithms. Our parallel approaches have two main processing phases. In the first processing phase, also called the Pre-processing Phase, we detect all the internal vertices of an input mesh. The Pre-processing Phase transforms an input mesh into a representation called a neighbor list, which is processed in parallel by the next processing phase, the GPU Processing Phase. Two parallel approaches for the Zhou and Shimada Smoothing Algorithm were attempted and are described here. One of these uses multiple threads per vertex to smooth by re-positioning internal vertices.;The other approach smoothes using a single thread per internal vertex. The multiple threads per internal vertex approach exhibits good performance in experiments we describe here. Speedups of GPU over CPU performance of the multiple thread per internal vertex approach range from 18.06 to 98.98 for the environment available to us. The single thread per internal vertex approach exhibits even better performance in experiments we describe here. Speedups of GPU over CPU performance for that single thread per internal vertex approach range from 28.38 to 154.57 for the environment available to us. The Xu and Newman Mesh Smoothing Algorithm was parallelized using a single thread per internal vertex approach. That approach exhibits a performance improvement over CPU performance in experiments we describe here. Speedups of GPU over CPU performance of that single thread per internal vertex approach range from 8.59 to 38.93 for the environment available to us.