GAMES Webinar 2021 – 183期(仿真模拟专题) | Xiaosong Chen (Tsinghua University), Cheng Li (Tencent)

【GAMES Webinar 2020-183期】(仿真模拟专题)

报告嘉宾1:Xiaosong Chen (Tsinghua University)


报告题目:A Moving Least Square Reproducing Kernel Particle Method for Unified Multiphase Continuum Simulation


In physically based-based animation, pure particle methods are popular due to their simple data structure, easy implementation, and convenient parallelization. As a pure particle-based method and using Galerkin discretization, the Moving Least Square Reproducing Kernel Method(MLSRK) was developed in engineering computation as a general numerical tool for solving PDEs. The basic idea of Moving Least Square(MLS) has also been used in computer graphics to estimatede formation gradient for deformable solids. Based on the seprevious studies, we propose a multiphase MLSRK framework that animates complex and coupled fluids and solids in a unified manner. Specifically, we use the Cauchy momentum equation and phase field model to uniformly capture the momentum balance and phase evolution/interaction in a multiphase system, and systematically formulate the MLSRK discretization to support general multiphase constitutive models. A series of animation examples are presented to demonstrate the performance of our new multiphase MLSRK framework,including hyperelastic, elastoplastic, viscous, fracturing and multiphase coupling behaviours etc.


Xiaosong Chen is a third-year master’s student at Tsinghua University, advised by Prof. Shi-Min Hu. He received his B.S. degree in Mathematics and Physics from Tsinghua University in 2018. His research interests lie in physically-based animation, mostly concerned with the simulation of varying multiphase materials and fluid control.


报告嘉宾2: Cheng Li (Tencent)


报告题目:P-Cloth: Interactive Cloth Simulation on Multi-GPU Systems using Dynamic Matrix Assembly and Pipelined Implicit Integrators


We present a novel parallel algorithm for cloth simulation that exploits multiple GPUs for fast computation and the handling of very high resolution meshes. To accelerate implicit integration, we describe new parallel algorithms for sparse matrix-vector multiplication (SpMV) and for dynamic matrix assembly on a multi-GPU workstation. Our algorithms use a novel work queue generation scheme for a fat-tree GPU interconnect topology. Furthermore, we present a novel collision handling scheme that uses spatial hashing for discrete and continuous collision detection along with a non-linear impact zone solver. Our parallel schemes can distribute the computation and storage overhead among multiple GPUs and enable us to perform almost interactive simulation on complex cloth meshes, which can hardly be handled on a single GPU due to memory limitations. We have evaluated the performance with two multi-GPU workstations (with 4 and 8 GPUs, respectively) on cloth meshes with 0.5 − 1.65M triangles. Our approach can reliably handle the collisions and generate vivid wrinkles and folds at 2 − 5 fps, which is significantly faster than prior cloth simulation systems. We observe almost linear speedups with respect to the number of GPUs.


Cheng Li is currently working at Tencent as a software engineer. He received his master’s degree from Zhejiang University, advised by Prof. Min Tang. His research interests include GPGPU, high performance computing and physical simulation.




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