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Plausible mass-spring system using parallel computing on mobile devices

机译:在移动设备上使用并行计算的合理质量弹簧系统

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摘要

Recently the hardware performance of mobile devices have been extremely increased and advanced mobile devices provide multi-cores and high clock speed. In addition, mobile devices have advantages in mobility and portability compared with PC and Console, so many games and simulation programs have been developed under mobile environments. Physically-based simulation is a one of the key issues for deformable object modeling which is widely used to represent the realistic expression of 3D soft objects with tetrahedrons for game and 3D simulation. However, it requires high computation power to plausibly and realistically represent the physical behaviors and interactions of deformable objects. In this paper, we implemented parallel cloth and mass-spring simulation using graphics processing unit (GPU) with OpenCL and multi-threaded central processing unit (CPU) on a mobile device. We applied CPU and GPU parallel computing technique into spring force computation and integration methods such as Euler, Midpoint, 4th-order Runge-Kutta to optimize the computational burden of dynamic simulation. The integration methods compute the next step of positions and velocities in each node. In this paper, we tested the performance analysis for the spring force calculation and integration method process using CPU only, multi-threaded CPU, and GPU on mobile device respectively. Our experimental results concluded that the calculation using proposed multi-threaded CPU and GPU multi-threaded CPU are much faster than using just the CPU only.
机译:最近,移动设备的硬件性能已得到极大提高,而先进的移动设备可提供多核和高时钟速度。另外,与PC和Console相比,移动设备在移动性和便携性方面具有优势,因此已经在移动环境下开发了许多游戏和模拟程序。基于物理的仿真是可变形对象建模的关键问题之一,可变形对象建模已广泛用于通过四面体来表示3D软对象的逼真表达,用于游戏和3D仿真。但是,它需要很高的计算能力才能真实,真实地表示可变形对象的物理行为和相互作用。在本文中,我们在移动设备上使用带有OpenCL的图形处理单元(GPU)和多线程中央处理单元(CPU)来实现并行布料和质量弹簧仿真。我们将CPU和GPU并行计算技术应用于Euler,Midpoint,四阶Runge-Kutta等弹力计算和集成方法,以优化动态仿真的计算负担。积分方法计算每个节点中位置和速度的下一步。在本文中,我们分别在移动设备上仅使用CPU,多线程CPU和GPU来测试弹簧力计算和集成方法过程的性能分析。我们的实验结果表明,使用建议的多线程CPU和GPU多线程CPU的计算比仅使用CPU的计算要快得多。

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