RAxML-NG is a phylogenetic tree inference tool which uses maximum-likelihood (ML) optimality criterion. Its search heuristic is based on iteratively performing a series of Subtree Pruning and Regrafting (SPR) moves, which allows to quickly navigate to the best-known ML tree. RAxML-NG is a successor of RAxML (Stamatakis 2014) and leverages the highly optimized likelihood computation implemented in libpll (Flouri et al. 2014). RAxML-NG offers improvements in speed, flexibility and user-friendliness over the previous RAxML versions. It also implements some of the features previously available in ExaML (Kozlov et al. 2015), including checkpointing and efficient load balancing for partitioned alignments (Kobert et al. 2014).

RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference Alexey M Kozlov, Diego Darriba, Tomás Flouri, Benoit Morel, Alexandros Stamatakis . Bioinformatics, Volume 35, Issue 21, 1 November 2019, Pages 4453-4455, https://doi.org/10.1093/bioinformatics/btz305

• raxml-ng on github
• raxml github wiki

• Module Name: raxml-ng (see the modules page for more information)
• Multithreaded, MPI
• Example files in /usr/local/apps/raxml-ng/examples

Allocate an interactive session and run the program.
Sample session (user input in bold):

[user@biowulf]$ sinteractive --cpus-per-task=4
salloc.exe: Pending job allocation 46116226
salloc.exe: job 46116226 queued and waiting for resources
salloc.exe: job 46116226 has been allocated resources
salloc.exe: Granted job allocation 46116226
salloc.exe: Waiting for resource configuration
salloc.exe: Nodes cn3144 are ready for job

[user@cn3144 ~]$ module load raxml-ng

[user@cn3144 ~]$  cp $RAXMLNG_EXAMPLE_DATA/myoglobins597.phy .

[user@cn3144 ~]$  raxml-ng --msa myoglobin597.phy --model GTR+G --threads $SLURM_CPUS_PER_TASK

[user@cn3144 ~]$ exit
salloc.exe: Relinquishing job allocation 46116226
[user@biowulf ~]$

Threaded version of raxml-ng

#!/bin/bash
set -e
module load raxml-ng

raxml-ng --msa myoglobin597.phy --model GTR+G --threads $SLURM_CPUS_PER_TASK

Submit this job using the SLURM sbatch command.

sbatch [--cpus-per-task=#] [--mem=#] [--time=DD-HH:MM:SS] raxml-ng.sh

MPI version of raxml-ng

In our tests, the threaded version ran faster than the MPI version. However, for larger input datasets the MPI version might perform better. Please run your own benchmark comparisons for your data, and let us know of any interesting results.

Here is sample batch script for an MPI-only job (but on a single node):

#!/bin/bash

module load raxml-ng/1.0.0-mpi
mpirun -n $SLURM_NTASKS raxml-ng-mpi --msa data.phy --model GTR+G --threads 1

Submit with:

sbatch --ntasks=# --ntasks-per-core=1 [--time=DD-HH:MM:SS] jobscript

Make sure to consult our documentation on efficient use of the multinode partition if you want to run raxml-ng-mpi over multiple nodes. Before you use the multinode parition make sure your dataset could benefit from such a large number of resources.

In most cases where you want to use MPI, the hybrid MPI/pthreads setup is more efficient. Please consult this page for how to set these up and reach out to us if needed.

Checkpointing is built in to raxml-ng. Thus, if you have a longrunning raxml-ng job that is terminated because you did not specify a long enough walltime, or if your job is going to require more than the Biowulf 10-day max walltime, you can resubmit the job and it will restart from the last checkpoint. See the advanced tutorial for details.

You can take advantage of this feature to run a chain of jobs, each of which will pick up where the previous one terminated, using job dependencies. For example:

biowulf% sbatch --cpus-per-task=16 --mem=20g  --time=8-00:00:00 myjobscript
1111
biowulf% sbatch --depend=afterany:1111 --cpus-per-task=16 --mem=20g  --time=8-00:00:00 myjobscript
2222
biowulf% sbatch --depend=afterany:2222 --cpus-per-task=16 --mem=20g  --time=8-00:00:00 myjobscript
3333

Create a swarmfile (e.g. raxml-ng.swarm). For example:

raxml-ng --msa file1.phy --model GTR+G --threads $SLURM_CPUS_PER_TASK
raxml-ng --msa file2.phy --model GTR+G --threads $SLURM_CPUS_PER_TASK
raxml-ng --msa file3.phy --model GTR+G --threads $SLURM_CPUS_PER_TASK
[...]

Submit this job using the swarm command.

swarm -f raxml-ng.swarm -g 20  -t 8  --module raxml-ng