How to Install and Uninstall llvm15-polly Package on openSuSE Tumbleweed
Last updated: November 23,2024
1. Install "llvm15-polly" package
This tutorial shows how to install llvm15-polly on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
llvm15-polly
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2. Uninstall "llvm15-polly" package
Please follow the guidelines below to uninstall llvm15-polly on openSuSE Tumbleweed:
$
sudo zypper remove
llvm15-polly
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3. Information about the llvm15-polly package on openSuSE Tumbleweed
Information for package llvm15-polly:
-------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : llvm15-polly
Version : 15.0.7-7.1
Arch : x86_64
Vendor : openSUSE
Installed Size : 4.7 MiB
Installed : No
Status : not installed
Source package : llvm15-15.0.7-7.1.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 : openSUSE-Tumbleweed-Oss
Name : llvm15-polly
Version : 15.0.7-7.1
Arch : x86_64
Vendor : openSUSE
Installed Size : 4.7 MiB
Installed : No
Status : not installed
Source package : llvm15-15.0.7-7.1.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.