GPU optimization of material point methods
GPU optimization of material point methods
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DOI：
10.1145/3272127.3275044
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发表时间：
2018-12
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通讯作者：
Ming Gao;Xinlei Wang;Kui Wu;Andre Pradhana;Eftychios Sifakis;Cem Yuksel;Chenfanfu Jiang
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文献类型：
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作者：
Ming Gao;Xinlei Wang;Kui Wu;Andre Pradhana;Eftychios Sifakis;Cem Yuksel;Chenfanfu Jiang
材料点方法（MPM）已被证明可以促进物理复杂和拓扑挑战性材料的有效模拟，在计算工程和可视化计算中具有丰富的新兴应用。由于规则性的极端重要性，MPM在高性能现代多处理器上获得了有吸引力的并行化机会。充分利用计算资源的MPM的并行化提出了挑战，需要为有利的数据结构和算法探索广泛的设计空间。与概念上简单的CPU并行化（可以轻松应用任务的粗略分区）不同，由于其众核SIMT架构，需要付出更大的努力才能达到GPU硬件饱和。在本文中，我们介绍了解决MPM的计算挑战和扩展基于MPM的通用仿真系统的能力，特别是集中在GPU优化的方法。除了我们的开源高性能框架外，我们还进行性能分析和基准测试实验，以比较表面上看起来合理的替代设计选择，但实际上可能会出现次优性能。我们的显式和全隐式GPU MPM解算器还配备了移动最小二乘MPM热解算器和新型砂土本构模型，可快速模拟各种材料。我们证明，超过一个数量级的性能提高，可以实现与我们的GPU求解器。实际的高分辨率示例，高达一千万个粒子，每帧运行时间不到一分钟。
The Material Point Method (MPM) has been shown to facilitate effective simulations of physically complex and topologically challenging materials, with a wealth of emerging applications in computational engineering and visual computing. Borne out of the extreme importance of regularity, MPM is given attractive parallelization opportunities on high-performance modern multiprocessors. Parallelization of MPM that fully leverages computing resources presents challenges that require exploring an extensive design-space for favorable data structures and algorithms. Unlike the conceptually simple CPU parallelization, where the coarse partition of tasks can be easily applied, it takes greater effort to reach the GPU hardware saturation due to its many-core SIMT architecture. In this paper we introduce methods for addressing the computational challenges of MPM and extending the capabilities of general simulation systems based on MPM, particularly concentrating on GPU optimization. In addition to our open-source high-performance framework, we also conduct performance analyses and benchmark experiments to compare against alternative design choices which may superficially appear to be reasonable, but can suffer from suboptimal performance in practice. Our explicit and fully implicit GPU MPM solvers are further equipped with a Moving Least Squares MPM heat solver and a novel sand constitutive model to enable fast simulations of a wide range of materials. We demonstrate that more than an order of magnitude performance improvement can be achieved with our GPU solvers. Practical high-resolution examples with up to ten million particles run in less than one minute per frame.