广义特征问题的矩阵符号函数并行谱划分

Int. J. High Speed Comput. Pub Date : 2000-03-01 DOI:10.1142/S0129053300000084

S. Huss-Lederman, E. S. Quintana‐Ortí, Xiaobai Sun, Yuan-Jye J. Wu

{"title":"广义特征问题的矩阵符号函数并行谱划分","authors":"S. Huss-Lederman, E. S. Quintana‐Ortí, Xiaobai Sun, Yuan-Jye J. Wu","doi":"10.1142/S0129053300000084","DOIUrl":null,"url":null,"abstract":"In this paper we demonstrate the parallelism of the spectral division using the matrix sign function for the generalized nonsymmetric eigenproblem. We employ the so–called generalized Newton iterative scheme in order to compute the sign function of the matrix pair. A recent study showed a considerable reduction (by 75%) in the computational cost of this iteration, making this approach competitive when compared to the traditional QZ algorithm. The experimental results on an IBM SP3 multicomputer report the parallel performance (efficiency around 60–80%) and scalability of this approach.","PeriodicalId":270006,"journal":{"name":"Int. J. High Speed Comput.","volume":"62 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Parallel Spectral Division Using the Matrix Sign Function for the Generalized Eigenproblem\",\"authors\":\"S. Huss-Lederman, E. S. Quintana‐Ortí, Xiaobai Sun, Yuan-Jye J. Wu\",\"doi\":\"10.1142/S0129053300000084\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we demonstrate the parallelism of the spectral division using the matrix sign function for the generalized nonsymmetric eigenproblem. We employ the so–called generalized Newton iterative scheme in order to compute the sign function of the matrix pair. A recent study showed a considerable reduction (by 75%) in the computational cost of this iteration, making this approach competitive when compared to the traditional QZ algorithm. The experimental results on an IBM SP3 multicomputer report the parallel performance (efficiency around 60–80%) and scalability of this approach.\",\"PeriodicalId\":270006,\"journal\":{\"name\":\"Int. J. High Speed Comput.\",\"volume\":\"62 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. High Speed Comput.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1142/S0129053300000084\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. High Speed Comput.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/S0129053300000084","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 10

摘要

本文利用矩阵符号函数证明了广义非对称特征问题谱分的并行性。为了计算矩阵对的符号函数，我们采用了所谓的广义牛顿迭代格式。最近的一项研究表明，该迭代的计算成本大幅降低(75%)，使该方法与传统的QZ算法相比具有竞争力。在IBM SP3多计算机上的实验结果报告了该方法的并行性能(效率约为60-80%)和可伸缩性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Parallel Spectral Division Using the Matrix Sign Function for the Generalized Eigenproblem

In this paper we demonstrate the parallelism of the spectral division using the matrix sign function for the generalized nonsymmetric eigenproblem. We employ the so–called generalized Newton iterative scheme in order to compute the sign function of the matrix pair. A recent study showed a considerable reduction (by 75%) in the computational cost of this iteration, making this approach competitive when compared to the traditional QZ algorithm. The experimental results on an IBM SP3 multicomputer report the parallel performance (efficiency around 60–80%) and scalability of this approach.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Int. J. High Speed Comput.

自引率

0.00%

发文量