These jobs offer a similar operation to LAPACK, but that has a major interface differing from that of LAPACK:
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LAPACK ("Linear Algebra Deal") is a standard software package library for numerical linear algebra. It offers routines for resolving methods of linear equations and linear minimum squares, eigenvalue challenges, and singular benefit decomposition. Furthermore, it features routines to implement the related matrix factorizations including LU, QR, Cholesky and Schur decomposition.
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Just like BLAS, LAPACK is typically forked or rewritten to provide greater performance on particular programs. A lot of the implementations are:
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A header library for linear algebra. Provides a BLAS and also a partial LAPACK implementation for compatibility.
LAPACK was developed as being the successor to your linear equations and linear minimum-squares routines of LINPACK as well as the eigenvalue routines of EISPACK. LINPACK, written during the seventies and 1980s, was made to operate around the then-fashionable vector personal computers with shared memory. LAPACK, in distinction, was built to successfully exploit the caches on fashionable cache-primarily based architectures as well as instruction-amount parallelism of contemporary superscalar processors,[2]: "Factors that Affect Performance" and therefore can operate orders of magnitude speedier than LINPACK on these types of machines, provided a well-tuned BLAS implementation.