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SIGGRAPH 2019 Courses

On Hybrid Lagrangian-Eulerian Simulation Methods:
Practical Notes and High-Performance Aspects

Yuanming Hu, Xinxin Zhang, Ming Gao, Chenfanfu Jiang

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Overview

This course is focused on practical high-performance implementations of hybrid Lagrangian-Eulerian simulation schemes, especially the Material Point Method (MPM) and hybrid vortex methods. Starting with a gentle introduction to the schemes and modern computer architecture, we will cover topics including algorithmic design, data structures, parallelization, and low-level architecture-specific CPU/GPU optimizations.

Slides [Download]

Code

Bibtex

@inproceedings{hu2019hybrid,
  title={On hybrid lagrangian-eulerian simulation methods: practical notes and high-performance aspects},
  author={Hu, Yuanming and Zhang, Xinxin and Gao, Ming and Jiang, Chenfanfu},
  booktitle={ACM SIGGRAPH 2019 Courses},
  pages={16},
  year={2019},
  organization={ACM}
}

Lecturers

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Yuanming Hu is a second-year Ph.D. student at MIT CSAIL with Fredo Durand and Bill Freeman. His research interests are physical simulation, computational photography, and high-performance computing. Currently, he works on programming languages and compilers for sparse visual computing.

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Xinxin Zhang did his PhD working with Robert Bridson at UBC. His  work mostly deals with N-Body summations and boundary integral solvers. He is currently a senior graphics RnD engineer at Tencent, before which he worked for Lytro, Beijing Film Academy, and got his first screen credit for Hobbits3: The Battle of Five Armies when he worked at Weta digital. He also open-sourced an efficient single file AMGPCG solver in case you are looking for a simple and good solution to your Poisson problems.

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Ming Gao got his Ph.D. from the University of Wisconsin, Madison working with Eftyvhios Sifakis, doing simulations with spatially adaptive fluid simulations and MPM. He joined Tencent America recently as a research scientist after his postdoctoral experience at the University of Pennsylvania.

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Chenfanfu Jiang is an assistant professor from the Univeristy of Pennsylvania. He got his PhD from UCLA and his research focus has been Particle-in-Cell fluids and MPM for more than five years. His lab is actively recruiting Ph.D. students and postdocs with interests and maybe experiences in particle simulations, high-performance computing, and GPU programming.