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Parallel techniques for physically based simulation on multi-core processor architectures

机译:多核处理器体系结构上基于物理的并行技术

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摘要

As multi-core processor systems become more and more widespread, the demand for efficient parallel algorithms also propagates into the field of computer graphics. This is especially true for physically based simulation, which is notorious for expensive numerical methods. In this work, we explore possibilities for accelerating physically based simulation algorithms on multi-core architectures. Two components of physically based simulation represent a great potential for bottlenecks in parallelisation: implicit time integration and collision handling. From the parallelisation point of view these two components are substantially different. Implicit time integration can be treated efficiently using static problem decomposition. The linear system arising in this context is solved using a data-parallel preconditioned conjugate gradient algorithm. The collision handling stage, however, requires a different approach, due to its dynamic structure. This stage is handled using multi-threaded programming with fully dynamic task decomposition. In particular, we propose a new task splitting approach based on a reasonable estimation of work, which analyses previous simulation steps. Altogether, the combination of different parallelisation techniques leads to a concise and yet versatile framework for highly efficient physical simulation.
机译:随着多核处理器系统越来越广泛,对高效并行算法的需求也传播到计算机图形学领域。对于基于物理的仿真尤其如此,因为它对于昂贵的数值方法而臭名昭著。在这项工作中,我们探索了在多核体系结构上加速基于物理的仿真算法的可能性。基于物理的仿真的两个组成部分表示并行化瓶颈的巨大潜力:隐式时间积分和冲突处理。从并行化的角度来看,这两个组件基本上是不同的。可以使用静态问题分解有效地处理隐式时间积分。使用数据并行预处理共轭梯度算法可以解决在这种情况下出现的线性系统。但是,由于其动态结构,碰撞处理阶段需要不同的方法。此阶段使用具有完全动态任务分解的多线程编程来处理。特别是,我们提出了一种基于合理工作估计的新任务拆分方法,该方法分析了先前的模拟步骤。总而言之,不同并行化技术的结合形成了一个简洁而又通用的框架,用于高效的物理仿真。

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