How to Install and Uninstall llvm16-polly Package on openSuSE Tumbleweed
Last updated: December 27,2024
1. Install "llvm16-polly" package
Please follow the instructions below to install llvm16-polly on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
llvm16-polly
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2. Uninstall "llvm16-polly" package
This guide let you learn how to uninstall llvm16-polly on openSuSE Tumbleweed:
$
sudo zypper remove
llvm16-polly
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3. Information about the llvm16-polly package on openSuSE Tumbleweed
Information for package llvm16-polly:
-------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : llvm16-polly
Version : 16.0.6-5.1
Arch : x86_64
Vendor : openSUSE
Installed Size : 4.7 MiB
Installed : No
Status : not installed
Source package : llvm16-16.0.6-5.1.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 : llvm16-polly
Version : 16.0.6-5.1
Arch : x86_64
Vendor : openSUSE
Installed Size : 4.7 MiB
Installed : No
Status : not installed
Source package : llvm16-16.0.6-5.1.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.