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.\parScaLAPACK includes routines for the solution of dense, band, and tridiagonal linear systems of equations, condition estimation and iterative refinement, for LU and Cholesky factorization, matrix inversion, full-rank linear least squares problems, orthogonal and generalized orthogonal factorizations, orthogonal transformation routines, reductions to upper Hessenberg, bidiagonal and tridiagonal form, reduction of a symmetric-definite/Hermitian-definite generalized eigenproblem to standard form, the symmetric/Hermitian, generalized symmetric/Hermitian, and the nonsymmetric eigenproblem. Prototype codes are provided for out-of-core solvers for LU, Cholesky, and QR, the matrix sign function for eigenproblems, and an HPF interface to a subset of ScaLAPACK routines.\parSoftware is available in single precision real, double precision real, single precision complex, and double precision complex. The software has been written to be portable across a wide range of distributed-memory environments such as the Cray T3, IBM SP, Intel series, TM CM-5, clusters of workstations, and any system for which PVM or MPI is available.\parEach 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 version of the library for a number of computers, example programs, and the full set of LAPACK Working Notes.

References in zbMATH (referenced in 377 articles , 3 standard articles )

Showing results 1 to 20 of 377.
Sorted by year (citations)

1 2 3 ... 17 18 19 next

  1. Tanaka, Kazuyuki; Imachi, Hiroto; Fukumoto, Tomoya; Kuwata, Akiyoshi; Harada, Yuki; Fukaya, Takeshi; Yamamoto, Yusaku; Hoshi, Takeo: EigenKernel (2019)
  2. Adlerborn, Björn; Karlsson, Lars; Kågström, Bo: Distributed one-stage Hessenberg-triangular reduction with wavefront scheduling (2018)
  3. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH
  4. Alzetta, Giovanni; Arndt, Daniel; Bangerth, Wolfgang; Boddu, Vishal; Brands, Benjamin; Davydov, Denis; Gassmöller, Rene; Heister, Timo; Heltai, Luca; Kormann, Katharina; Kronbichler, Martin; Maier, Matthias; Pelteret, Jean-Paul; Turcksin, Bruno; Wells, David: The deal.II library, version 9.0 (2018)
  5. Aparinov, A. A.; Setukha, A. V.; Stavtsev, S. L.: Parallel implementation for some applications of integral equations method (2018)
  6. Dongarra, Jack; Gates, Mark; Haidar, Azzam; Kurzak, Jakub; Luszczek, Piotr; Tomov, Stanimire; Yamazaki, Ichitaro: The singular value decomposition: anatomy of optimizing an algorithm for extreme scale (2018)
  7. Drmač, Zlatko; Saibaba, Arvind Krishna: The discrete empirical interpolation method: canonical structure and formulation in weighted inner product spaces (2018)
  8. Kalantzis, Vassilis; Xi, Yuanzhe; Saad, Yousef: Beyond automated multilevel substructuring: domain decomposition with rational filtering (2018)
  9. Li, Shengguo; Rouet, François-Henry; Liu, Jie; Huang, Chun; Gao, Xingyu; Chi, Xuebin: An efficient hybrid tridiagonal divide-and-conquer algorithm on distributed memory architectures (2018)
  10. Ralha, Rui: Mixed precision bisection (2018)
  11. Barber, Xavier; Conesa, David; López-Quílez, Antonio; Mayoral, Asunción; Morales, Javier; Barber, Antoni: Bayesian hierarchical models for analysing the spatial distribution of bioclimatic indices (2017)
  12. Carlberg, Kevin; Barone, Matthew; Antil, Harbir: Galerkin v. least-squares Petrov-Galerkin projection in nonlinear model reduction (2017)
  13. Damle, Anil; Lin, Lin; Ying, Lexing: Computing localized representations of the Kohn-Sham subspace via randomization and refinement (2017)
  14. Filippone, Salvatore; Cardellini, Valeria; Barbieri, Davide; Fanfarillo, Alessandro: Sparse matrix-vector multiplication on GPGPUs (2017)
  15. Gentle, James E.: Matrix algebra. Theory, computations and applications in statistics (2017)
  16. Grigori, Laura: Introduction to communication avoiding algorithms for direct methods of factorization in linear algebra (2017)
  17. Hadjiantoni, Stella; Kontoghiorghes, Erricos John: Estimating large-scale general linear and seemingly unrelated regressions models after deleting observations (2017)
  18. Jacquelin, Mathias; Lin, Lin; Yang, Chao: \textttPselinv-- a distributed memory parallel algorithm for selected inversion, the symmetric case (2017)
  19. Liska, Sebastian; Colonius, Tim: A fast immersed boundary method for external incompressible viscous flows using lattice Green’s functions (2017)
  20. Marx, Alain; Lütjens, Hinrich: Hybrid parallelization of the XTOR-2F code for the simulation of two-fluid MHD instabilities in tokamaks (2017)

1 2 3 ... 17 18 19 next