A bunch of procedures, common procs and APIs

M3 Example SLURM Scripts

1. Python "Hello World" Example

For instance, let's say that I want to print "Hello world", using python. I'll create a simple python script, say, helloworld.py, which contains just the single line:

print("Hello world!")

And another separate SLURM script, helloworld.sh, which looks like: ```


SBATCH --job-name=helloworld

SBATCH --ntasks=1

SBATCH --ntasks-per-node=1

SBATCH --cpus-per-task=1

SBATCH --time=0-01:00:00

SBATCH --mail-user=don.teng@monash.edu

SBATCH --mail-type=END

SBATCH --mail-type=FAIL

module load python/3.5.2-gcc5 python3 helloworld.py ```

And in the M3 terminal, I enter the following command to submit the job:

sbatch helloworld.sh

(Note that I'm submitting the .sh file, which is the SLURM script. The .py file is a python script.

2. BEAST Example

BEAST is a bit of software which computes phylogenetic trees within a Bayesian statistical framework (that's what the "B" in 'BEAST' stands for). It accepts as input an xml file in a specific format (you can use BEAUTI to create this format), and returns a phylogenetic tree (a .tree file) and a couple of other secondary output files like a log file. We usually run BEAST either using the graphical user-interface (GUI), or on the command line. For instance, to run BEAST on our local machine, we'd use something like:

beast -beagle_CPU my_input_file.xml

To interpret that: beast tells the terminal to do something doing BEAST, -beagle_CPU tells the terminal to use the BEAGLE library if it's available (which it should be on the HPC), and my_input_file.xml is, obviously, the input file to run BEAST, in .xml format.

(To try this out, you can download dummy data from BEAST, called benchmark1.xml and benchmark2.xml, from their benchmark webpage. We know the output of these datasets, so these are used to gauge the speed of different hardware setups, so that people can publish their running results with different configurations, e.g. number of CPUs, number of GPUs, etc.)

The SLURM script is as follows::

Usage: sbatch beast_bm1_cpu.sh

#SBATCH --job-name=beast_bm1

#SBATCH --ntasks=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=2

#SBATCH --time=0-01:00:00

#SBATCH --mail-user=don.teng@monash.edu
#SBATCH --mail-type=END
#SBATCH --mail-type=FAIL

module load beast1/1.8.4
module load beagle
beast -beagle_CPU benchmark1.xml

2b. BEAST BEAGLE with the GPU

The BEAGLE library is a helper program to BEAST to speed up computations. It can do this in a few ways - this section tells you how to utilize the GPU.

(A CPU is the "normal" kind of processing unit present in all computers. A Graphical-Processing-Unit (GPU) is an additional bit of hardware, popular amongst gamers because computer games require extremely powerful processing units capable of rendering 3D environments.)

The computers (or "nodes") on the HPC that have GPUs are in the partition m3c and m3f. Aside from the usual #SBATCH parameters specified above, you'll have to include a couple more #SBATCH lines. The run command is also quite different:

#SBATCH --gres=gpu:1
#SBATCH --partition=m3c

module load beast1/1.8.4
module load beagle/2.1.2
module switch cuda/7.0

beast -beagle_info
beast -beagle_cuda -beagle_order 1,0 benchmark1.xml

Coming soon: -beagle_SSE

3. RAxML Example

RAxML is the trickiest to use because, in a way, there is no quickstart command that can apply to most cases - tweaking RAxML to be applicable to the study requirements is a giant can of worms. The example given below is not likely to be very fast for most application - in particular, run time increases exponentially as the number and length of sequences increases. RaxML isn't written to utilize GPUs, so don't bother sending your job to GPU partitions.

A "default" run command is:

#SBATCH --ntasks=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=4

raxmlHPC-PTHREADS -T 4 -f a -m GTRGAMMA -p 12345 -x 12345 -N 200 -s input_file.fasta -n output_file.txt

That is: this job is split into 4 processes, which get distributed to 4 CPU cores in 1 node.