How to Install and Uninstall kokkos.x86_64 Package on Red Hat Enterprise Linux 8 (RHEL 8)
Last updated: November 16,2024
1. Install "kokkos.x86_64" package
Please follow the guidelines below to install kokkos.x86_64 on Red Hat Enterprise Linux 8 (RHEL 8)
$
sudo dnf update
Copied
$
sudo dnf install
kokkos.x86_64
Copied
2. Uninstall "kokkos.x86_64" package
In this section, we are going to explain the necessary steps to uninstall kokkos.x86_64 on Red Hat Enterprise Linux 8 (RHEL 8):
$
sudo dnf remove
kokkos.x86_64
Copied
$
sudo dnf autoremove
Copied
3. Information about the kokkos.x86_64 package on Red Hat Enterprise Linux 8 (RHEL 8)
Last metadata expiration check: 0:05:51 ago on Mon Feb 26 15:59:38 2024.
Available Packages
Name : kokkos
Version : 3.0.00
Release : 2.el8
Architecture : x86_64
Size : 71 k
Source : kokkos-3.0.00-2.el8.src.rpm
Repository : epel
Summary : Kokkos C++ Performance Portability Programming
URL : https://github.com/kokkos/kokkos
License : BSD
Description :
: Kokkos Core implements a programming model in C++ for writing performance
: portable applications targeting all major HPC platforms. For that purpose
: it provides abstractions for both parallel execution of code and data
: management. Kokkos is designed to target complex node architectures with
: N-level memory hierarchies and multiple types of execution resources. It
: currently can use OpenMP, Pthreads and CUDA as backend programming models.
Available Packages
Name : kokkos
Version : 3.0.00
Release : 2.el8
Architecture : x86_64
Size : 71 k
Source : kokkos-3.0.00-2.el8.src.rpm
Repository : epel
Summary : Kokkos C++ Performance Portability Programming
URL : https://github.com/kokkos/kokkos
License : BSD
Description :
: Kokkos Core implements a programming model in C++ for writing performance
: portable applications targeting all major HPC platforms. For that purpose
: it provides abstractions for both parallel execution of code and data
: management. Kokkos is designed to target complex node architectures with
: N-level memory hierarchies and multiple types of execution resources. It
: currently can use OpenMP, Pthreads and CUDA as backend programming models.