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L. S. Blackford, J. Choi, A. Cleary, E. D'Azevedo, J. Demmel, I. Dhillon, J. Dongarra, S. Hammarling, G. Henry, A. Petitet, K. Stanley, D. Walker, R. C. Whaley 1 May 1997 Dedication This work is dedicated to the pioneers of high-performance computing who blazed a trail, set standards, and made our job easier. Acknowledgment We give credit to and thank all of the LAPACK authors for allowing us to r

Click here to see the number of accesses to this library. SVDPACK comprises four numerical (iterative) methods for computing the singular value decomposition (SVD) of large sparse matrices using double precision ANSI Fortran-77. A compatible ANSI-C version (SVDPACKC) is also available. This software package implements Lanczos and subspace iteration-based methods for determining several of the larg

Emeritus Professor University of Tennessee Department of Electrical Engineering and Computer Science Former Director Innovative Computing Laboratory Former Director Center for Information Technology Research Distinguished Research Staff Oak Ridge National Laboratory Computer Science and Mathematics Division Contact Information Jack Dongarra Electrical Engineering and Computer Science Department 11

file: links.html for: related resources file: dtoa.c by: David Gay for: ANSI C or C++ source for functions strtod and dtoa that do decimal-to-binary and binary-to-decimal conversions. Comments at the beginning describe various preprocessor variables that can be defined to make this code work with binary IEEE, VAX, or IBM-mainframe arithmetic. A paper is available at https://9p.io/cm/cs/doc/90/4-10

The BLAS (Basic Linear Algebra Subprograms) are routines that provide standard building blocks for performing basic vector and matrix operations. The Level 1 BLAS perform scalar, vector and vector-vector operations, the Level 2 BLAS perform matrix-vector operations, and the Level 3 BLAS perform matrix-matrix operations. Because the BLAS are efficient, portable, and widely available, they are commo

This applet runs the Linpack Benchmark in Java. The Linpack Benchmark is a numerically intensive test that has been used for years to measure the floating point performance of computers. To run the benchmark after the graph loads hit "Press to run ...". Jack Dongarra, Reed Wade, and Paul McMahan (please direct inquiries to dongarra@cs.utk.edu) A number of people have been confused by the results o

HPL - A Portable Implementation of the High-Performance Linpack Benchmark for Distributed-Memory Computers HPL is a software package that solves a (random) dense linear system in double precision (64 bits) arithmetic on distributed-memory computers. It can thus be regarded as a portable as well as freely available implementation of the High Performance Computing Linpack Benchmark. The algorithm us

HPL - A Portable Implementation of the High-Performance Linpack Benchmark for Distributed-Memory Computers HPL is a software package that solves a (random) dense linear system in double precision (64 bits) arithmetic on distributed-memory computers. It can thus be regarded as a portable as well as freely available implementation of the High Performance Computing Linpack Benchmark. The algorithm us

############################################################################ # PLEASE NOTE: # # # # THE CBLAS ARE NOT PROVIDED BY NETLIB WHEN CLAPACK ROUTINES ARE REQUESTED.# # # # It is assumed that an optimized version of the BLAS are already # # present on your machine. If this is not the case, please refer # # to the clapack/cblas directory. # ##################################################

LAPACK is a software package provided by Univ. of Tennessee; Univ. of California, Berkeley; Univ. of Colorado Denver; and NAG Ltd.. LAPACK is written in Fortran 90 and provides routines for solving systems of simultaneous linear equations, least-squares solutions of linear systems of equations, eigenvalue problems, and singular value problems. The associated matrix factorizations (LU, Cholesky, QR