How to Install and Uninstall libzita-convolver4-32bit Package on openSuSE Tumbleweed
Last updated: November 23,2024
Deprecated! Installation of this package may no longer be supported.
1. Install "libzita-convolver4-32bit" package
Learn how to install libzita-convolver4-32bit on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
libzita-convolver4-32bit
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2. Uninstall "libzita-convolver4-32bit" package
Please follow the instructions below to uninstall libzita-convolver4-32bit on openSuSE Tumbleweed:
$
sudo zypper remove
libzita-convolver4-32bit
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3. Information about the libzita-convolver4-32bit package on openSuSE Tumbleweed
Information for package libzita-convolver4-32bit:
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Repository : openSUSE-Tumbleweed-Oss
Name : libzita-convolver4-32bit
Version : 4.0.3-1.12
Arch : x86_64
Vendor : openSUSE
Installed Size : 29,8 KiB
Installed : No
Status : not installed
Source package : zita-convolver-4.0.3-1.12.src
Summary : A partitioned convolution engine library
Description :
Convolution engine based on FFT convolution and using non-uniform partition
sizes: small ones at the start of the IR and building up to the most efficient
size further on. It can perform zero-delay processing with moderate CPU load.
Main features:
* Any matrix of convolutions between up to up 64 inputs and 64 outputs, as
long as your CPU(s) can handle it.
* Allows trading off CPU load to processing delay, and remains efficient even
when configured for zero delay.
* Sparse and diagonal matrices are handled as efficiently as dense ones.
No CPU cycles or memory resources are wasted on empty cells in the matrix,
nor on empty partitions if IRs are of different length.
-------------------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : libzita-convolver4-32bit
Version : 4.0.3-1.12
Arch : x86_64
Vendor : openSUSE
Installed Size : 29,8 KiB
Installed : No
Status : not installed
Source package : zita-convolver-4.0.3-1.12.src
Summary : A partitioned convolution engine library
Description :
Convolution engine based on FFT convolution and using non-uniform partition
sizes: small ones at the start of the IR and building up to the most efficient
size further on. It can perform zero-delay processing with moderate CPU load.
Main features:
* Any matrix of convolutions between up to up 64 inputs and 64 outputs, as
long as your CPU(s) can handle it.
* Allows trading off CPU load to processing delay, and remains efficient even
when configured for zero delay.
* Sparse and diagonal matrices are handled as efficiently as dense ones.
No CPU cycles or memory resources are wasted on empty cells in the matrix,
nor on empty partitions if IRs are of different length.