How to Install and Uninstall llvm9-polly Package on openSuSE Tumbleweed
Last updated: March 04,2025
Deprecated! Installation of this package may no longer be supported.
1. Install "llvm9-polly" package
Please follow the steps below to install llvm9-polly on openSuSE Tumbleweed
$
sudo zypper refresh
Copied
$
sudo zypper install
llvm9-polly
Copied
2. Uninstall "llvm9-polly" package
Please follow the guidance below to uninstall llvm9-polly on openSuSE Tumbleweed:
$
sudo zypper remove
llvm9-polly
Copied
3. Information about the llvm9-polly package on openSuSE Tumbleweed
Information for package llvm9-polly:
------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : llvm9-polly
Version : 9.0.1-19.9
Arch : x86_64
Vendor : openSUSE
Installed Size : 4,5 MiB
Installed : No
Status : not installed
Source package : llvm9-9.0.1-19.9.src
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 : llvm9-polly
Version : 9.0.1-19.9
Arch : x86_64
Vendor : openSUSE
Installed Size : 4,5 MiB
Installed : No
Status : not installed
Source package : llvm9-9.0.1-19.9.src
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.