Study of a parallel algorithm on pipelined computation of the finite difference schemes on FPGA

Wenshi Wang, Zhangqin Huang, Shuo Zhang
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Abstract

With the rapid increase of data, stream computation contributes to the improvement of data real time processing. One of characters of stream computing is in the pipeline to exploit parallelism in the MIMD way. Moreover, pipelined parallel implementation is a common problem and often yields much better parallel efficiency with respect to other commonly used methods. Plentiful physical modeling in the field of image processing and machine vision deals with the solution of partial differential equations, typical representative by Poisson's equation. The finite difference schemes as the main method for the solution of partial difference equation for physical modeling can be time-consuming and computationally expensive. Constructing highly parallel computation models can greatly solve the problem and it is important that quickly and efficiently carry out it. The paper mainly studies the parallel implementation of the mix of Jacobi iterative and Gauss-Seidel method for the finite difference schemes to solve Poisson equations in a pipelined fashion on FPGA.
基于FPGA的有限差分格式并行计算算法研究
随着数据量的快速增长,流计算有助于提高数据的实时性。流计算的特点之一是在管道中利用MIMD方式的并行性。此外,流水线并行实现是一个常见的问题,相对于其他常用方法,它通常产生更好的并行效率。在图像处理和机器视觉领域中,大量的物理建模涉及偏微分方程的求解,以泊松方程为典型代表。有限差分格式作为求解物理建模偏差分方程的主要方法,耗时长,计算量大。构建高度并行的计算模型可以极大地解决这一问题,快速有效地实现这一问题至关重要。本文主要研究了用Jacobi迭代法和gaas - seidel方法混合求解泊松方程的有限差分格式在FPGA上的并行实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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