How to Install and Uninstall libmetis_5_1_0-gnu-hpc Package on openSuSE Tumbleweed
Last updated: November 07,2024
1. Install "libmetis_5_1_0-gnu-hpc" package
Please follow the step by step instructions below to install libmetis_5_1_0-gnu-hpc on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
libmetis_5_1_0-gnu-hpc
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2. Uninstall "libmetis_5_1_0-gnu-hpc" package
In this section, we are going to explain the necessary steps to uninstall libmetis_5_1_0-gnu-hpc on openSuSE Tumbleweed:
$
sudo zypper remove
libmetis_5_1_0-gnu-hpc
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3. Information about the libmetis_5_1_0-gnu-hpc package on openSuSE Tumbleweed
Information for package libmetis_5_1_0-gnu-hpc:
-----------------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : libmetis_5_1_0-gnu-hpc
Version : 5.1.0-10.1
Arch : x86_64
Vendor : openSUSE
Installed Size : 447.8 KiB
Installed : No
Status : not installed
Source package : metis_5_1_0-gnu-hpc-5.1.0-10.1.src
Upstream URL : http://glaros.dtc.umn.edu/gkhome/metis/metis/overview
Summary : Serial Graph Partitioning and Fill-reducing Matrix Ordering library
Description :
METIS library provides to partitioning unstructured graphs and hypergraph
and computing fill-reducing orderings of sparse matrices. The underlying algorithms
used by METIS are based on a multilevel paradigm that, at the time, had been
shown to produce quality results and scale to large problems.
-----------------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : libmetis_5_1_0-gnu-hpc
Version : 5.1.0-10.1
Arch : x86_64
Vendor : openSUSE
Installed Size : 447.8 KiB
Installed : No
Status : not installed
Source package : metis_5_1_0-gnu-hpc-5.1.0-10.1.src
Upstream URL : http://glaros.dtc.umn.edu/gkhome/metis/metis/overview
Summary : Serial Graph Partitioning and Fill-reducing Matrix Ordering library
Description :
METIS library provides to partitioning unstructured graphs and hypergraph
and computing fill-reducing orderings of sparse matrices. The underlying algorithms
used by METIS are based on a multilevel paradigm that, at the time, had been
shown to produce quality results and scale to large problems.