ScaLAPACK Users' Guide (Software, Environments and Tools)

ScaLAPACK is an acronym for Scalable Linear Algebra Package or Scalable LAPACK. It is a library of high-performance linear algebra routines for distributed memory message-passing MIMD computers and networks of workstations supporting parallel virtual machine (PVM) and/or message passing interface (MPI). It is a continuation of the LAPACK project, which designed and produced analogous software for workstations, vector supercomputers, and shared memory parallel computers. Both libraries contain routines for solving systems of linear equations, least squares problems, and eigenvalue problems. The goals of both projects are efficiency, scalability, reliability, portability, flexibility, and ease of use. Each Users' Guide includes a CD-ROM containing the HTML version of the ScaLAPACK Users' Guide, the source code for the package, testing and timing programs, prebuilt versions of the library for a number of computers, example programs, and the full set of LAPACK Working Notes. La 4e de couverture indique : "ScaLAPACK is an acronyme for Scalable Linear Algebra or Scalable LAPACK. It is a library of high-performance linear algrebra routines for distributed-memory message-passing MIMD computers and networks of workstations supporting parallel virtual machine (PVM) and/or message passing interface (MPI). It is a continuation of the LAPACK project, which designed and produced analogous software for workstations, vector supercomputers, and shared memory parallel computers. Both libraries contain routines for solving systems of linear equations, least squares problems, and eigenvalue problems. The goals of both projects are efficiency, scalability, reliability, portability, flexibility, and ease of use. ScaLAPACK includes routines for solution of dense, band, and tridiagonal linear systems of equations, condition estimarion and iterative refinement, for LU and Cholesky factorization, matrix inversion, full-rank linear least squares problems, orthogonal and generalized orthogonal factorizations, orthogonal transformation routines, reduction to upper hessenberg, bidiagonal and tridiagonal form, reduction of symmetric-definite/Hermitian-definite generalized eigenproblem to standars form, the symmetric/hermitian, generalized symmetric/Hermitian, and nonsymmetric eigenproblem, and the singular value decomposition.[...]." L.s. Blackford ... [et Al.]. Includes Bibliographical References (p.305-313) And Index.