A Parallel Crout Algorithm based on TBB

Liyan Zhang, Yan Sun, Jian Ma
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引用次数: 2

Abstract

The paper presents a novel Parallel Crout Algorithm (PCA) based on multi-core computer with Threading Building Blocks (TBB). TBB offers a rich and complete approach to express parallelism in a C++ program. PCA is decomposed into three-tier: data decomposition parallelism, task processing parallelism and data composition parallelism and it can improve the efficiency of solving linear systems. Compared with Sequential Crout Algorithm (SCA), PCA has advantages of high efficiency, cross-platform and scalability. SCA and PCA, which is based on TBB, are implemented with C++. The validities of both methods are verified by different scale of matrix. In order to improve decomposition rate, the paper optimizes the parameters of PCA. Experiments show that, compared with SCA, PCA can reached a faster solution speed and a higher efficiency and it takes full advantage of Symmetrical Multi-Processing computer.
基于TBB的并行Crout算法
提出了一种基于线程构建块(TBB)的多核计算机并行分组算法(PCA)。TBB提供了一种在c++程序中表达并行性的丰富而完整的方法。PCA被分解为数据分解并行性、任务处理并行性和数据组合并行性三层,可以提高求解线性系统的效率。与顺序分组算法(SCA)相比,PCA具有高效、跨平台和可扩展性等优点。基于TBB的SCA和PCA采用c++实现。通过不同尺度的矩阵验证了两种方法的有效性。为了提高分解率,本文对主成分分析的参数进行了优化。实验表明,与SCA相比,PCA可以达到更快的求解速度和更高的效率,并充分利用了对称多处理计算机的优势。
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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