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Constant Boundary Elements on graphics hardware: a GPU-CPU complementary implementation

机译:图形硬件上的恒定边界元素:GPU-CPU补充实现

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Numerical simulation of engineering problems has reached such a large scale that the use of a parallel computing approach is required to obtain solutions within a reasonable time. Recent efforts have been made to implement these large scale computational tasks on general-purpose programmable graphics hardware (GPGPU). The Graphics Processing Unit (GPU) is specially well-suited to address problems that can be formulated in form of data-parallel computations with high arithmetic intensity. This work addresses the implementation of the direct version of the Boundary Element Method (DBEM) on a complementary GPU-CPU system. In this article, constant elements were used for the solution of 2D potential problems. A serial implementation of the BEM was rewritten under the SIMT (Single Instruction Multiple Thread) parallel programming paradigm. The code was developed on an NVidiaTM CUDA programming environment. The efficiency of the implemented strategies is investigated by solving a representative 2D potential problem. The paper reviews in detail the classical BEM formulation in order to be able to address the possible parallelization steps in the numerical implementation. The article reports the performance of the GPU-CPU system compared to the classical CPU-based system for an increasing number of boundary elements.
机译:工程问题的数值模拟已经达到了如此大规模,以至于需要使用并行计算方法才能在合理的时间内获得解决方案。为了在通用可编程图形硬件(GPGPU)上实现这些大规模计算任务,最近进行了努力。图形处理单元(GPU)特别适合解决可以以高算术强度以数据并行计算形式提出的问题。这项工作解决了在互补的GPU-CPU系统上实现边界元素方法(DBEM)的直接版本的问题。在本文中,常量元素用于解决2D潜在问题。 BEM的串行实现是在SIMT(单指令多线程)并行编程范例下重写的。该代码是在NVidiaTM CUDA编程环境上开发的。通过解决代表性的2D潜在问题,研究了已实施策略的效率。本文详细回顾了经典的BEM公式,以便能够解决数值实现中可能的并行化步骤。这篇文章报告了与越来越多的边界元素相比,GPU-CPU系统与传统的基于CPU的系统相比的性能。

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