Topic
EISPACK
About: EISPACK is a research topic. Over the lifetime, 280 publications have been published within this topic receiving 14639 citations.
Papers published on a yearly basis
1 / 2
Papers
Book•
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1 Feb 1995
TL;DR: The third edition of LAPACK provided a guide to troubleshooting and installation of Routines, as well as providing examples of how to convert from LINPACK or EISPACK to BLAS.
Abstract: Preface to the third edition Preface to the secondedition Part 1. Guide. 1. Essentials 2. Contents of LAPACK 3. Performance of LAPACK 4. Accuracy and Stability 5. Documentation and Software Conventions 6. Installing LAPACK Routines 7. Troubleshooting Appendix A. Index of Driver and Computational Routines Appendix B. Index of Auxiliary Routines Appendix C. Quick Reference Guide to the BLAS Appendix D. Converting from LINPACK or EISPACK Appendix E. LAPACK Working Notes Part 2. Specifications of Routines. Bibliography Index by Keyword Index by Routine Name.
3,276 citations
Book•
1 Jan 1974
TL;DR: Eispack as discussed by the authors is an Eispack subroutine that uses handbook algol procedures to validate and validate EISPACKs and is used for EisPacks.
Abstract: How to use Eispack.- Validation of Eispack.- Execution times for Eispack.- Certification and availability of Eispack.- Differences between the Eispack subroutines and the handbook algol procedures.- Documentation and source listings.
967 citations
641 citations
1 Oct 1990
TL;DR: The goal of the LAPACK project is to design and implement a portable linear algebra library for efficient use on a variety of high-performance computers, based on the widely used LINPACK and EISPACK packages, but extends their functionality in a number of ways.
Abstract: The goal of the LAPACK project is to design and implement a portable linear algebra library for efficient use on a variety of high-performance computers. The library is based on the widely used LINPACK and EISPACK packages for solving linear equations, eigenvalue problems, and linear least-squares problems, but extends their functionality in a number of ways. The major methodology for making the algorithms run faster is to restructure them to perform block matrix operations (e.g., matrix-matrix multiplication) in their inner loops. These block operations may be optimized to exploit the memory hierarchy of a specific architecture. The LAPACK project is also working on new algorithms that yield higher relative accuracy for a variety of linear algebra problems. >
516 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2020 | 1 |
| 2019 | 1 |
| 2018 | 2 |
| 2017 | 3 |
| 2016 | 6 |
| 2015 | 4 |