How to Install and Uninstall examl Package on Ubuntu 21.04 (Hirsute Hippo)

Last updated: November 07,2024

1. Install "examl" package

Here is a brief guide to show you how to install examl on Ubuntu 21.04 (Hirsute Hippo)

$ sudo apt update $ sudo apt install examl

2. Uninstall "examl" package

Please follow the step by step instructions below to uninstall examl on Ubuntu 21.04 (Hirsute Hippo):

$ sudo apt remove examl $ sudo apt autoclean && sudo apt autoremove

3. Information about the examl package on Ubuntu 21.04 (Hirsute Hippo)

Package: examl
Architecture: amd64
Version: 3.0.22-1
Built-Using: simde (= 0.0.0.git.20200526-1)
Priority: optional
Section: universe/science
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Med Packaging Team
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 6293
Depends: libc6 (>= 2.29), libopenmpi3 (>= 4.0.3), openmpi-bin
Filename: pool/universe/e/examl/examl_3.0.22-1_amd64.deb
Size: 951332
MD5sum: 49d9476b998dd734826c1a19f5c7228a
SHA1: 57898389f16d6203c2375d16aa4059288a5e6b49
SHA256: 43562fa1a7794421e5b357c064f41e14b18ea1dfce9dd9dca4b263053aa4f47e
SHA512: 439d67e5ee5bf2c714c678c8879b4e910bc39430ffd408d6ecdc735477b5881cf1577dc61857db6375ac507b649e0f2a44645cb3a8de0caad3e7109c27a04dbd
Homepage: https://github.com/stamatak/ExaML
Description-en: Exascale Maximum Likelihood (ExaML) code for phylogenetic inference
Exascale Maximum Likelihood (ExaML) is a code for phylogenetic inference
using MPI. This code implements the popular RAxML search algorithm for
maximum likelihood based inference of phylogenetic trees.
.
ExaML is a strapped-down light-weight version of RAxML for phylogenetic
inference on huge datasets. It can only execute some very basic
functions and is intended for computer-savvy users that can write little
perl-scripts and have experience using queue submission scripts for
clusters. ExaML only implements the CAT and GAMMA models of rate
heterogeneity for binary, DNA, and protein data.
.
ExaML uses a radically new MPI parallelization approach that yields
improved parallel efficiency, in particular on partitioned multi-gene or
whole-genome datasets. It also implements a new load balancing algorithm
that yields better parallel efficiency.
.
It is up to 4 times faster than its predecessor RAxML-Light and scales
to a larger number of processors.
Description-md5: 4972cdf739509b60035d9f3b35bafb60