Squeezing the most out of eigenvalue solvers on high-performance computers

Jack J. Dongarra; Linda Kaufman; Sven Hammarling

doi:10.1016/0024-3795(86)90164-3

Squeezing the most out of eigenvalue solvers on high-performance computers

Jack J. Dongarra, Linda Kaufman, Sven Hammarling

Research output: Contribution to journal › Article › peer-review

24 Scopus citations

Abstract

This paper describes modifications to many of the standard algorithms used in computing eigenvalues and eigenvectors of matrices. These modifications can dramatically increase the performance of the underlying software on high-performance computers without resorting to assembler language, without significantly influencing the floating-point operation count, and without affecting the roundoff-error properties of the algorithms. The techniques are applied to a wide variety of algorithms and are beneficial in various architectural settings.

Original language	English
Pages (from-to)	113-136
Number of pages	24
Journal	Linear Algebra and Its Applications
Volume	77
Issue number	C
DOIs	https://doi.org/10.1016/0024-3795(86)90164-3
State	Published - May 1986
Externally published	Yes

Funding

*Work supported in part by the Applied Mathematical Sciences subprogram of the Office of Energy Research, U.S. Department of Energy, under Contract W-31-109Eng.38.

Funders	Funder number
Office of Energy Research
U.S. Department of Energy	W-31-109Eng.38

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10.1016/0024-3795(86)90164-3

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abstract = "This paper describes modifications to many of the standard algorithms used in computing eigenvalues and eigenvectors of matrices. These modifications can dramatically increase the performance of the underlying software on high-performance computers without resorting to assembler language, without significantly influencing the floating-point operation count, and without affecting the roundoff-error properties of the algorithms. The techniques are applied to a wide variety of algorithms and are beneficial in various architectural settings.",

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AB - This paper describes modifications to many of the standard algorithms used in computing eigenvalues and eigenvectors of matrices. These modifications can dramatically increase the performance of the underlying software on high-performance computers without resorting to assembler language, without significantly influencing the floating-point operation count, and without affecting the roundoff-error properties of the algorithms. The techniques are applied to a wide variety of algorithms and are beneficial in various architectural settings.

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Squeezing the most out of eigenvalue solvers on high-performance computers

Abstract

Funding

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