How to Install and Uninstall llvm9-polly Package on openSUSE Leap
Last updated: November 21,2024
1. Install "llvm9-polly" package
Here is a brief guide to show you how to install llvm9-polly on openSUSE Leap
$
sudo zypper refresh
Copied
$
sudo zypper install
llvm9-polly
Copied
2. Uninstall "llvm9-polly" package
Learn how to uninstall llvm9-polly on openSUSE Leap:
$
sudo zypper remove
llvm9-polly
Copied
3. Information about the llvm9-polly package on openSUSE Leap
Information for package llvm9-polly:
------------------------------------
Repository : Main Repository
Name : llvm9-polly
Version : 9.0.1-150200.3.6.1
Arch : x86_64
Vendor : SUSE LLC
Installed Size : 4.5 MiB
Installed : No
Status : not installed
Source package : llvm9-9.0.1-150200.3.6.1.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 : llvm9-polly
Version : 9.0.1-150200.3.6.1
Arch : x86_64
Vendor : SUSE LLC
Installed Size : 4.5 MiB
Installed : No
Status : not installed
Source package : llvm9-9.0.1-150200.3.6.1.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.