How to Install and Uninstall llvm11-polly Package on openSUSE Leap
Last updated: November 21,2024
1. Install "llvm11-polly" package
Please follow the step by step instructions below to install llvm11-polly on openSUSE Leap
$
sudo zypper refresh
Copied
$
sudo zypper install
llvm11-polly
Copied
2. Uninstall "llvm11-polly" package
Please follow the guidelines below to uninstall llvm11-polly on openSUSE Leap:
$
sudo zypper remove
llvm11-polly
Copied
3. Information about the llvm11-polly package on openSUSE Leap
Information for package llvm11-polly:
-------------------------------------
Repository : Main Repository
Name : llvm11-polly
Version : 11.0.1-150300.3.6.1
Arch : x86_64
Vendor : SUSE LLC
Installed Size : 4.6 MiB
Installed : No
Status : not installed
Source package : llvm11-11.0.1-150300.3.6.1.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 : Main Repository
Name : llvm11-polly
Version : 11.0.1-150300.3.6.1
Arch : x86_64
Vendor : SUSE LLC
Installed Size : 4.6 MiB
Installed : No
Status : not installed
Source package : llvm11-11.0.1-150300.3.6.1.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.