高质量的实时图像到网格的有限元模拟转换

Panagiotis A. Foteinos, N. Chrisochoides
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引用次数: 42

摘要

在这张海报中,我们提出了一种并行图像到网格转换(I2M)算法,通过动态点插入和移除来保证质量和保真度。直接从图像开始,它能够恢复表面,并用良好形状的四面体网格化体积。我们的紧密耦合共享内存并行推测执行范例采用了精心设计的内存和争用管理器、负载平衡、同步和优化方案,同时保持了较高的单线程性能:我们的单线程性能比CGAL更快,CGAL是我们所知道的最先进的串行I2M软件。我们的网格也有理论上的保证:半径边缘小于2,边界三角形的平面角大于30度。在匹兹堡超级计算中心的大型缓存相干NUMA机器Blacklight上验证了该方法的有效性。我们观察到在128核的情况下,超过74%的强缩放效率和超线性的弱缩放效率。
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High Quality Real-Time Image-to-Mesh Conversion for Finite Element Simulations
In this poster, we present a parallel Image-to-Mesh Conversion (I2M) algorithm with quality and fidelity guarantees achieved by dynamic point insertions and removals. Starting directly from an image, it is able to recover the surface and mesh the volume with tetrahedra of good shape. Our tightly-coupled shared-memory parallel speculative execution paradigm employs carefully designed memory and contention managers, load balancing, synchronization and optimizations schemes, while it maintains high single-threaded performance: our single-threaded performance is faster than CGAL, the state of the art sequential I2M software we are aware of. Our meshes come also with theoretical guarantees: the radius-edge is less than 2 and the planar angles of the boundary triangles are more than 30 degrees. The effectiveness of our method is shown on Blacklight, the large cache-coherent NUMA machine of Pittsburgh Supercomputing Center. We observe a more than 74% strong scaling efficiency for up to 128 cores and a super-linear weak scaling efficiency for up to 128 cores.
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