【摘要】 GaBP(Gaussian Belief Propagation)是一种解线性代数方程组的迭代算法,它是基于递归更新的概率推理算法,具有低复杂性和高并行性.MIC是英特尔的至强融核Xeon Phi的Many Integerated Core架构.它提供数百个同时运行的硬件线程,能充分满足对高并发度的大量需求.本文研究了如何高效地求解大规模稀疏线性方程组的并行算法,通过挖掘GaBP算法特性,优化算法存储结构和加速迭代,同时给出了一种求解大规模稀疏对称线性方程组的基于MIC的GaBP并行算法;并从美国Florida.大学开发的稀疏矩阵库(UFget)中抽取了部分大规模对称稀疏矩阵作为算例进行测试,计算结果表明,在相同精度下,基于MIC的GaBP并行算法相对于GaBP算法具有更显著的高效率.更多还原

【Abstract】 A new algorithm named MIC-GABP algorithm is proposed for solving large scale symmetric sparse linear system.This algorithm is based on GaBP(Gauss Belief Propagation)and MIC(Many Integerated Core).We take several large scale sparse matrices from The University of Florida Sparse Matrix Collection as examples to observe the performance of our algorithm.The experimental result shows that MIC-GaBP algorithm has a higher efficiency than traditional GaBP algorithm under the same accuracy.更多还原