How to Install and Uninstall llvm13-polly Package on openSUSE Leap
Last updated: September 28,2024
1. Install "llvm13-polly" package
Please follow the guidance below to install llvm13-polly on openSUSE Leap
$
sudo zypper refresh
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$
sudo zypper install
llvm13-polly
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2. Uninstall "llvm13-polly" package
In this section, we are going to explain the necessary steps to uninstall llvm13-polly on openSUSE Leap:
$
sudo zypper remove
llvm13-polly
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3. Information about the llvm13-polly package on openSUSE Leap
Information for package llvm13-polly:
-------------------------------------
Repository : Main Repository
Name : llvm13-polly
Version : 13.0.1-bp155.6.38
Arch : x86_64
Vendor : openSUSE
Installed Size : 4.7 MiB
Installed : No
Status : not installed
Source package : llvm13-13.0.1-bp155.6.38.src
Upstream URL : https://polly.llvm.org/
Summary : LLVM Framework for High-Level Loop and Data-Locality Optimizations
Description :
Polly is a high-level loop and data-locality optimizer and optimization
infrastructure for LLVM. It uses an abstract mathematical representation based
on integer polyhedra to analyze and optimize the memory access pattern of a
program. Polly can currently perform classical loop transformations, especially
tiling and loop fusion to improve data-locality. It can also exploit OpenMP
level parallelism and expose SIMDization opportunities.
-------------------------------------
Repository : Main Repository
Name : llvm13-polly
Version : 13.0.1-bp155.6.38
Arch : x86_64
Vendor : openSUSE
Installed Size : 4.7 MiB
Installed : No
Status : not installed
Source package : llvm13-13.0.1-bp155.6.38.src
Upstream URL : https://polly.llvm.org/
Summary : LLVM Framework for High-Level Loop and Data-Locality Optimizations
Description :
Polly is a high-level loop and data-locality optimizer and optimization
infrastructure for LLVM. It uses an abstract mathematical representation based
on integer polyhedra to analyze and optimize the memory access pattern of a
program. Polly can currently perform classical loop transformations, especially
tiling and loop fusion to improve data-locality. It can also exploit OpenMP
level parallelism and expose SIMDization opportunities.