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.

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  1. Hoshi, Takeo; Ogita, Takeshi; Ozaki, Katsuhisa; Terao, Takeshi: An a posteriori verification method for generalized real-symmetric eigenvalue problems in large-scale electronic state calculations (2020)
  2. Reguly, István Z.; Mudalige, Gihan R.: Productivity, performance, and portability for computational fluid dynamics applications (2020)
  3. Amestoy, Patrick R.; de la Kethulle de Ryhove, Sébastien; L’Excellent, Jean-Yves; Moreau, Gilles; Shantsev, Daniil V.: Efficient use of sparsity by direct solvers applied to 3D controlled-source EM problems (2019)
  4. Arndt, Daniel; Bangerth, Wolfgang; Clevenger, Thomas C.; Davydov, Denis; Fehling, Marc; Garcia-Sanchez, Daniel; Harper, Graham; Heister, Timo; Heltai, Luca; Kronbichler, Martin; Kynch, Ross Maguire; Maier, Matthias; Pelteret, Jean-Paul; Turcksin, Bruno; Wells, David: The deal.II library, Version 9.1 (2019)
  5. Dongarra, Jack; Gates, Mark; Haidar, Azzam; Kurzak, Jakub; Luszczek, Piotr; Wu, Panruo; Yamazaki, Ichitaro; Yarkhan, Asim; Abalenkovs, Maksims; Bagherpour, Negin; Hammarling, Sven; Šístek, Jakub; Stevens, David; Zounon, Mawussi; Relton, Samuel D.: PLASMA: Parallel linear algebra software for multicore using OpenMP (2019)
  6. Gorman, Christopher; Chávez, Gustavo; Ghysels, Pieter; Mary, Théo; Rouet, François-Henry; Li, Xiaoye Sherry: Robust and accurate stopping criteria for adaptive randomized sampling in Matrix-free hierarchically semiseparable construction (2019)
  7. Kurzak, Jakub; Gates, Mark; Charara, Ali; Yarkhan, Asim; Yamazaki, Ichitaro; Dongarra, Jack: Linear systems solvers for distributed-memory machines with GPU accelerators (2019)
  8. Lin, Lin; Lu, Jianfeng; Ying, Lexing: Numerical methods for Kohn-Sham density functional theory (2019)
  9. Mohanty, Sraban Kumar; Sajith, G.: An input/output efficient algorithm for Hessenberg reduction (2019)
  10. Sahai, Tuhin; Ziessler, Adrian; Klus, Stefan; Dellnitz, Michael: Continuous relaxations for the traveling salesman problem (2019)
  11. Sukkari, Dalal; Ltaief, Hatem; Esposito, Aniello; Keyes, David: A QDWH-based SVD software framework on distributed-memory manycore systems (2019)
  12. Tanaka, Kazuyuki; Imachi, Hiroto; Fukumoto, Tomoya; Kuwata, Akiyoshi; Harada, Yuki; Fukaya, Takeshi; Yamamoto, Yusaku; Hoshi, Takeo: EigenKernel (2019)
  13. Winkelmann, Jan; Springer, Paul; Di Napoli, Edoardo: ChASE: Chebyshev accelerated subspace iteration eigensolver for sequences of Hermitian eigenvalue problems (2019)
  14. Wu, Rongteng; Xie, Xiaohong: A heterogeneous parallel LU factorization algorithm based on a basic column block uniform allocation strategy (2019)
  15. Adlerborn, Björn; Karlsson, Lars; Kågström, Bo: Distributed one-stage Hessenberg-triangular reduction with wavefront scheduling (2018)
  16. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH
  17. 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)
  18. Aparinov, A. A.; Setukha, A. V.; Stavtsev, S. L.: Parallel implementation for some applications of integral equations method (2018)
  19. 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)
  20. Drmač, Zlatko; Saibaba, Arvind Krishna: The discrete empirical interpolation method: canonical structure and formulation in weighted inner product spaces (2018)

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