How to Install and Uninstall metis_5_1_0-gnu-hpc Package on openSuSE Tumbleweed
Last updated: November 17,2024
1. Install "metis_5_1_0-gnu-hpc" package
In this section, we are going to explain the necessary steps to install metis_5_1_0-gnu-hpc on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
metis_5_1_0-gnu-hpc
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2. Uninstall "metis_5_1_0-gnu-hpc" package
Here is a brief guide to show you how to uninstall metis_5_1_0-gnu-hpc on openSuSE Tumbleweed:
$
sudo zypper remove
metis_5_1_0-gnu-hpc
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3. Information about the metis_5_1_0-gnu-hpc package on openSuSE Tumbleweed
Information for package metis_5_1_0-gnu-hpc:
--------------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : metis_5_1_0-gnu-hpc
Version : 5.1.0-10.1
Arch : x86_64
Vendor : openSUSE
Installed Size : 246.3 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
Description :
METIS is a family of programs for 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 : metis_5_1_0-gnu-hpc
Version : 5.1.0-10.1
Arch : x86_64
Vendor : openSUSE
Installed Size : 246.3 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
Description :
METIS is a family of programs for 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.