本文首先介绍了稀疏矩阵的特点和研究稀疏矩阵分解的意义，接着讨论了稀疏矩阵各种快速算法并给出了本文所采用的方法。在此基础上详细说明了稀疏矩阵模拟排序算法，直接LU分解算法，符号LU分解算法，数值LU分解算法及这些算法在FPGA上的实现过程。最后为充分发挥FPGA作为一种可编程逻辑器件的优势，将单核数值LU分解扩展为多核并行LU分解结构，并使用BDB矩阵对该结构进行了验证，给出并分析了实验结果。-Firstly,the characteristies and research value of sparse matrix are introduced．Then we describe various methods that have been explored in the past for speeding-up the

process of LU factorization，on the basis of which our designs al e presented including

sparse ordering methods，LU factorization，symbolic LU factorization,numerical LU factorization and their realization based on FPGA．Finally,by taking advantage of FPGA resources we implement a parallel LU factorization which iS verified by BDB sparse matrix and we analyze the experimental results