How to Install and Uninstall ocaml-parmap Package on openSuSE Tumbleweed
Last updated: December 25,2024
1. Install "ocaml-parmap" package
This guide let you learn how to install ocaml-parmap on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
ocaml-parmap
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2. Uninstall "ocaml-parmap" package
This guide covers the steps necessary to uninstall ocaml-parmap on openSuSE Tumbleweed:
$
sudo zypper remove
ocaml-parmap
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3. Information about the ocaml-parmap package on openSuSE Tumbleweed
Information for package ocaml-parmap:
-------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : ocaml-parmap
Version : 1.2.5-2.2
Arch : x86_64
Vendor : openSUSE
Installed Size : 203.0 KiB
Installed : No
Status : not installed
Source package : ocaml-parmap-1.2.5-2.2.src
Upstream URL : https://opam.ocaml.org/packages/parmap
Summary : Multicore architecture exploitation for OCaml programs with minimal modifications
Description :
If you want to use your many cores to accelerate an operation
which happens to be a map, fold or map/fold (map-reduce), just use
Parmap's parmap, parfold and parmapfold primitives in place of the
standard List.map and friends; you can specify the number of
subprocesses to use with the optional parameter ncores, and the
size of granularity of the parallel computation with the optional
parameter chunksize.
-------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : ocaml-parmap
Version : 1.2.5-2.2
Arch : x86_64
Vendor : openSUSE
Installed Size : 203.0 KiB
Installed : No
Status : not installed
Source package : ocaml-parmap-1.2.5-2.2.src
Upstream URL : https://opam.ocaml.org/packages/parmap
Summary : Multicore architecture exploitation for OCaml programs with minimal modifications
Description :
If you want to use your many cores to accelerate an operation
which happens to be a map, fold or map/fold (map-reduce), just use
Parmap's parmap, parfold and parmapfold primitives in place of the
standard List.map and friends; you can specify the number of
subprocesses to use with the optional parameter ncores, and the
size of granularity of the parallel computation with the optional
parameter chunksize.