How to Install and Uninstall llvm10-polly Package on openSuSE Tumbleweed
Last updated: November 23,2024
Deprecated! Installation of this package may no longer be supported.
1. Install "llvm10-polly" package
This guide covers the steps necessary to install llvm10-polly on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
llvm10-polly
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2. Uninstall "llvm10-polly" package
This guide let you learn how to uninstall llvm10-polly on openSuSE Tumbleweed:
$
sudo zypper remove
llvm10-polly
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3. Information about the llvm10-polly package on openSuSE Tumbleweed
Information for package llvm10-polly:
-------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : llvm10-polly
Version : 10.0.1-8.15
Arch : x86_64
Vendor : openSUSE
Installed Size : 4,5 MiB
Installed : No
Status : not installed
Source package : llvm10-10.0.1-8.15.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 : llvm10-polly
Version : 10.0.1-8.15
Arch : x86_64
Vendor : openSUSE
Installed Size : 4,5 MiB
Installed : No
Status : not installed
Source package : llvm10-10.0.1-8.15.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.