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amgcl package

amgcl 1.4.3-5build1 source package in Ubuntu

Changelog

amgcl (1.4.3-5build1) noble; urgency=medium

  * No-change rebuild with Python 3.12 as default

 -- Graham Inggs <email address hidden>  Fri, 19 Jan 2024 18:54:47 +0000

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Uploaded by:
Graham Inggs
Uploaded to:
Noble
Original maintainer:
Debian Science Team
Architectures:
any all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Oracular release multiverse misc
Noble release multiverse misc

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File Size SHA-256 Checksum
amgcl_1.4.3.orig.tar.gz 2.9 MiB e920d5767814ce697d707d1f359a16c9b9eb79eba28fe19e14c18c2a505fe0ad
amgcl_1.4.3-5build1.debian.tar.xz 6.1 KiB 94fc03f2c05bda09662173cb9da4af71a60d08753bbd997d339442dce9518010
amgcl_1.4.3-5build1.dsc 2.3 KiB 53a14a9bb15ca32aa0a62dfd0acd9b61fad818209981f85569616e917aed1c2a

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Binary packages built by this source

libamgcl-dev: Solves large sparse linear systems with algebraic multigrid method

 AMG is one of the most effective iterative methods for solution of equation
 systems arising, for example, from discretizing PDEs on unstructured grids. The
 method can be used as a black-box solver for various computational problems,
 since it does not require any information about the underlying geometry. AMG is
 often used not as a standalone solver but as a preconditioner within an
 iterative solver (e.g. Conjugate Gradients, BiCGStab, or GMRES).
 .
 AMGCL builds the AMG hierarchy on a CPU and then transfers it to one of the
 provided backends. This allows for transparent acceleration of the solution
 phase with help of OpenCL, CUDA, or OpenMP technologies. Users may provide
 their own backends which enables tight integration between AMGCL and the user
 code.
 .
 AMG is a header-only C++ library, with the headers provided by this package.

python3-amgcl: Solves large sparse linear systems with algebraic multigrid method

 AMG is one of the most effective iterative methods for solution of equation
 systems arising, for example, from discretizing PDEs on unstructured grids. The
 method can be used as a black-box solver for various computational problems,
 since it does not require any information about the underlying geometry. AMG is
 often used not as a standalone solver but as a preconditioner within an
 iterative solver (e.g. Conjugate Gradients, BiCGStab, or GMRES).
 .
 AMGCL builds the AMG hierarchy on a CPU and then transfers it to one of the
 provided backends. This allows for transparent acceleration of the solution
 phase with help of OpenCL, CUDA, or OpenMP technologies. Users may provide
 their own backends which enables tight integration between AMGCL and the user
 code.
 .
 This package provides the Python interface