The Crunchomics’ Documentation¶

Crunchomics: The Genomics Compute Environment for SILS and IBED¶

Cruchomics has 1 application server, a web server, a head node and 5 compute nodes. A storage system is mounted in which all users have a fast 25 GB SSD home directory and a 500 GB home directory. Volumes from the faculty server can be mounted on the system.

CPU: AMD EPYC Rome 7302 32 cores, 64 threads, 3GHz
Compute cluster: 160 cores, 320 threads
Infiniband internal connection
Local storage compute cluster: 8TB (SSD/NVMe): /scratch
- /scratch is emptied after month of inactivity
Memory:
- Web server: 256 GB
- Application server: 1024 GB
- Head node and compute nodes: 512 GB
Storage: gross 504 TB operated in RAID-Z2: net approx. 220 TB.
- If one disk fails: no data loss
- Snapshots taken: some protection against unintentional file deletions
- No backups made!
File systems are mounted on all nodes.
OS: CentOS 7
Help: w.c.deleeuw@uva.nl / j.rauwerda@uva.nl

Log in on the Head Node¶

Remark: if you are outside UvA, use VPN.
from your local (desktop or laptop) machine
- start puTTY or mobyxterm (windows)
- start terminal program (mac)
- start xterm or konsole (linux)

On Windows¶

Connect via MobaXterm (includes X11 graphical output) or PuTTY.
MobaXterm facilitates to remember passwords.
Via MobaXterm interface (address is omics-h0.science.uva.nl):

Login Without a Password with RSA Keys¶

An explanation on how to use SSH keys (existing ones or newly generated) can be found here: SSH keys with PuTTY
If you don’t want to use the Remember Password mechanism in MobaXterm but still do not want to supply your password each time you login, you can configure MobaXterm also for use of SSH keys.
- Start a local terminal with MobaXterm and type:
  if [ -f ~/.ssh/id_rsa.pub ] ; then echo "Public key found."; else ssh-keygen -t rsa ; fi
- Type enter 3 times unless you want to use a passfrase for the key. Copy de public part key to the file .ssh/authorized_keys on omics-h0. Here you have to type the password a last time. In your local terminal type:
  cat ~/.ssh/id_rsa.pub | \ ssh uvanetid1@omics-h0.science.uva.nl \ 'mkdir -p .ssh && cat >> .ssh/authorized_keys'
- You can use the ssh key pair in MobaXterm by giving the location of the private key (it is in your local ‘persistent’ home directory, normally but depending on your windows version C:\Users\<YOUR_WINDOWS_LOGIN_NAME>\DOCUMENTS\MobaXterm\home\.ssh)
- Or: use the key generator mechanism in MobaXterm

On Linux and Mac¶

Via a terminal:

#replace uvanetid1 with your uvanetid:
ssh -X uvanetid1@omics-h0.science.uva.nl
# -X for X11 graphical output
xclock
# if you see a clock xwindows works

Login Without a Password with RSA Keys¶

Login using rsa keys: on the local system generate key if it doesn’t exist:

if [  -f ~/.ssh/id_rsa.pub ] ; \
        then  echo "Public key found.";
        else ssh-keygen -t rsa ;
fi

Type enter 3 times unless you want to use a passfrase for the key. Copy de public part key to the file .ssh/aurhorized_keys on omics-h0. Here you have to type the password a last time:
```
cat ~/.ssh/id_rsa.pub  | \
ssh uvanetid1@omics-h0.science.uva.nl \
'mkdir -p .ssh && cat >> .ssh/authorized_keys'
```
Test if it works. Now you should be logged without being asked a password:
```
ssh uvanetid1@omics-h0.science.uva.nl
```

Preparing Your Account¶

Getting Your Environment Ready¶

You want to:
- use system wide installed software: /zfs/omics/software/bin is added to your path
- have a python3 environment available + (genomics) specific packages such as pandas and snakemake installed: madpy3 can be used as a command to activate the madpy3 environment
- have a more informative prompt
- have, in your ultrafast home directory a link to your 500 GB personal directory on the file storage.

A script to do the things mentioned above for you is here, type on your head node shell:

/zfs/omics/software/script/omics_install_script

This script will change the .bashrc and .bash_profile files such that the requirements mentioned above are fullfilled.

DIY¶

If you want to make the adaptations yourself, here are some ideas:
Create a shortcut (softlink) in your home directory (25GB) to your personal directory (500 GB):
```
ln -s /zfs/omics/personal/$USER ~/personal
```
Update the PATH variable in your .bashrc file so you can use the software installed in /zfs/omics/software. This is software not installed by the package manager, such as R, bowtie2, samtools etc.:
```
export PATH=${PATH}:/zfs/omics/software/bin
```
Some tools, such as snakemake, htseq, etc. need python3 and can be executed in a python virtual environment.

Activate the virtual environment as follows:

which snakemake
#which: no snakemake
source /zfs/omics/software/v_envs/madpy3/bin/activate
which snakemake
#/zfs/omics/software/v_envs/madpy3/bin/snakemake
deactivate

Account¶

The quota for storage are:
- 25GB in your home directory
- 500GB in /zfs/omics/personal/$USER
The quota also apply to snapshots. Snapshots are made daily at 00.00.00 and kept for 2 weeks. This means that deleting files which are in a snapshot will not be available for another 2 weeks. It also means that if you accidentally remove a file it can be restored up to 2 weeks after removal.
Data on Crunchomics is stored on multiple disks. Therefore, there is protection against disk failure. The data is not replicated. SILS users are encouraged to put their raw data on tape as soon as these are produced: SILS tape archive. Tape storage is duplicated.
Help: w.c.deleeuw@uva.nl / j.rauwerda@uva.nl

The Crunchomics Application Server¶

The address of the application server is omics-app01.science.uva.nl and you can log in to it in a similar fashion as you log in to the headnode. Your home and personal directory as well as other locations on the file system are also available on the application server.

CLC Genomics Workbench¶

log in on the application server: address is omics-app01.science.uva.nl (not the headnode!!):
```
#in terminal type:
clcgenomicswb21
```
Remember, your home directory is 25G, so it is advisable to make your default location somewhere on /zfs/omics/personal/uvanetid1/
- add a new folder in CLC genomics workbench (e.g. /zfs/omics/personal/*uvanetid1*/CLC_personal)
- and make it the default location

Start RStudio¶

We are currently setting up a log in to RStudio with your uvanetid via Surfconnect. This Surfconnect access is not available yet. Until then you can login directly on the application server.
- Login to RStudio on the application server with your UvAnetid and password at: http://omics-app01.science.uva.nl:8787/. Don’t forget to include the port number (8787) in your url.

The Crunchomics Compute Cluster¶

Interactive access¶

Interactive work on the head node omics-h0.science.uva.nl is possible but only for small computational tasks. All other jobs should be executed via the SLURM queue!!.

You can run interactive jobs on the head node
- small jobs
- parameterization of a big job, pilot jobs
- Only run jobs interactively when they take less than 12 hrs execution time on one cpu.
If you have a job for which a really large amount of memory is needed, such as a genome assembly, you can use the app server that has 1TB memory installed.
- Mark that the compute nodes have a 512 GB memory each, so many assembly jobs can better be put to the compute cluster.

Example of Interactive R Sessions¶

connect to the head node (with X), start R

a simple example in the kick-off meeting (in R):

t=seq(0,10,0.1)
y=sin(t)
plot(t,y,type="l", xlab="time", ylab="Sine wave")

Another example: * a file is read (the result of a BLAST alignment) and the ratio of the query length and subject length are plotted, together with x=y:

xclock&
wget https://surfdrive.surf.nl/files/index.php/s/9xIik2oVfjA8VVg/download \
--output-document blp.txt

Next, in R

blp<-read.table("blp.txt", header=T)
head(blp)
x11(type="cairo")
library(ggplot2)
lbl<-paste("average ratio:",round(mean(blp$qlen/blp$slen),2))
lbl
plot1<-ggplot(blp,aes(qlen,slen,colour=log(bitscore))) +
#scale_colour_gradientn(colours=rainbow(8)) +
scale_colour_gradient(low="lightgreen",high="darkgreen") +
geom_point(size=0.5, alpha=18/20, shape=20) +
coord_cartesian(ylim = c(0, 1000),xlim =  c(0,1000)) +
geom_abline(slope=1, intercept=0, alpha=0.8, colour="red") +
labs(x = "query length", y="subject length", title = "Blast result predicted proteins on protein database") +
annotate("text", label = lbl, x = 800, y = 10)
plot1
#png("/zfs/omics/personal/jrauwer1/Crunchomics_intro/plot_blast_qlen_slen.png",width=700, height=700, type="cairo" )
#plot1
#dev.off()

Miniconda¶

Conda is an open source package management system and environment management system. Conda allows you find and install packages in your own environment. You don’t need administrator privileges.
If you need a package that requires a different version of Python, you use conda as an environment manager. With just a few commands, you can set up a totally separate environment to run that different version of Python, while continuing to run your usual version of Python in your normal environment.

Installation¶

basic install is 89M
other packages will require additional space, so rather install it in /zfs/omics/personal/<uvanetid1> than in your /home/<uvanetid1>

Get miniconda¶

Download it

cd /zfs/omics/personal/${USER}
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh

Install Miniconda¶

Run the install script (as you)

bash Miniconda3-latest-Linux-x86_64.sh

Read the license information and answer yes….. After you answered yes, you will see a message like:

Specify here /zfs/omics/personal/${USER}/miniconda3 (or replace \${USER} with your uvanetid).
When the installation has finished you are asked if you want to initialize conda. Answer yes:

This finishes the installation:

After the installation, to have a shell with conda, leave your current one and start a new one or type:

source ~/.bashrc

After you have done this, you will see (base) as a prefix in your shell prompt.
So, each time you start a shell, conda will be activated, if you don’t want that, type:

conda config --set auto_activate_base fals

How much space does miniconda3 take?

cd /zfs/personal/${USER}/
du -hs miniconda3/
#437M    miniconda3/

Installation of a Conda Package¶

Suppose, you want to make a bacterial assembly with flye. With which flye you discover that flye is not installed on Crunchomics. Of course, you can ask the maintainers of Crunchomics to install flye. But you also could install flye as a conda package. You go to https://anaconda.org/ and search for flye
There is a conda package available:

When you click on flye you get a link with installation details:

Suppose you don’t want flye in your base environment. Therefore, you create a new environment that you call nptools. Install flye in the nptools evironment, deactivate base and activate nptools:

which flye  #indeed, no flye
conda create -n nptools
conda install -n nptools -c bioconda flye
conda deactivate
conda activate nptools
flye -h
#usage: flye (--pacbio-raw | --pacbio-corr | --pacbio-hifi | --nano-raw |
#             --nano-corr | --subassemblies) file1 [file_2 ...]
#             --genome-size SIZE --out-dir PATH
conda env list
conda deactivate
flye
#bash: flye: command not found
#
du -hs /zfs/personal/${USER}/miniconda3/
#820M    miniconda3/

Look what is in your environment with:

conda list -n nptools
conda list -n base

Example: Using Conda Installation of Flye¶

conda deactivate
conda activate nptools
cd /zfs/omics/personal/${USER}/
mkdir -p ecoli
cd ecoli
wget https://zenodo.org/record/1172816/files/Loman_E.coli_MAP006-1_2D_50x.fasta
date
flye --nano-raw Loman_E.coli_MAP006-1_2D_50x.fasta --out-dir . --threads 4
date

Slurm Overview¶

Jobs on the compute cluster are scheduled by a queue manager or cluster workload manager, called Slurm.
Slurm has three key functions.
1. It allocates exclusive and/or non-exclusive access to resources (compute nodes) to users for some duration of time.
2. It provides a framework for starting, executing, and monitoring work (normally a parallel job) on the set of allocated nodes.
3. It arbitrates contention for resources by managing a queue of pending work.
Although Slurm offers several ways to restrict the use of resources to a maximum, currently Crunchomics is set up to give users a maximum amount of freedom. It is very easy to claim significant parts or even the whole cluster for extended periods of time. We expect users to use this freedom wisely and allow other users to do their work. Keep an eye on the overall system usage while running big jobs. Also free allocations which are not used. If you plan to run very big projects (10.000+ core-hours) you are encouraged to contact us beforehand to discuss distribution of the work in time. If we notice a user is using too much resources we will contact him or her. If necessary we can put restrictions on usage.

Additional help and information¶

This guide only covers a basic introduction/overview of Slurm with a focus on subjects which are specific to the Crunchomics cluster and bioinformatics problems and targeted at people who have some familiarity with the command line. Lots of information about slurm and the commands to use slurm is availabe from other sources:

Detailed help information with the slurm commands is available, e.g. sinfo --help
Nice tutorials and intros on the web, e.g. Slurm quick start.

Quick slurm demo¶

Information about the cluster¶

sinfo to get information about the cluster: node names, state, partition (queue)
- partition: the queues that are available
- state: idle (available for jobs), mix (partly available), down etc. Slurm codes
- node list: the names of the nodes omics-cn001 to omics-cn005

squeue: view information about jobs in the queue
- JOBID: every job gets a number. You can manipulate jobs via this number (e.g. with scancel)
- ST: state, R = running, PD = pending, see: Slurm state codes.

Multithreading¶

Example of running a multithreaded program, which will be used to illustrate the use of slurm further below. We want to map a collection of reads (READS) to an an organism (DB) and write the result to RESFILE. (/dev/null means the data is discarded)

#You could run this program interactively on omics-h0.science.uva.nl
export READS="/zfs/omics/software/doc/Crunchomics_intro/DemoData/sample_1M.fastq"
export DB="/zfs/omics/software/doc/Crunchomics_intro/DemoData/db/NC_000913_ncbi"
export RESFILE="/dev/null"
#
# single thread:
bowtie2 -t  -p 1  -x  $DB -U $READS >  $RESFILE
# multi threaded  use 4 threads:
bowtie2 -t  -p 4  -x  $DB -U $READS >  $RESFILE

srun¶

However, it is more efficient to run this mapping on the compute cluster using the same command in slurm with srun executed from the head node omics-h0.science.uva.nl:

# run bowtie as a single job with 16 cores:
srun -n 1 --cpus-per-task 16  bowtie2 -t -p 16  -x  $DB -U $READS  >  $RESFILE
# also possible: run an interactive  shell on a compute node, in this case for 10 minutes:
srun --pty -t 10 bash -i

Allocation of the compute resource is for the duration of the command.

salloc¶

Often a job consists of a number of steps (jobsteps)

Explicit allocation with salloc for interactive running of several jobs in row.

# allocate 16 cpu's on a single node, in this case for 30 minutes
salloc -n 1 -t 30 --cpus-per-task 16 bash
# the shell runs on the headnode srun is used to use the allocation
# srun uses the available cpu's in the allocation and using
srun bowtie2 -t  -p 16  -x  $DB -U $READS  >  $RESFILE
# with salloc we set 16 cpus-per-task.
# Slurm holds this information in the variable $SLURM_CPUS_PER_TASK,
# which we can use in the bowtie command:
srun bowtie2 -t  -p $SLURM_CPUS_PER_TASK  -x  $DB -U $READS  >  $RESFILE
#
# more SLURM variables in the environment:
env | grep SLURM
#
# it is not a good idea to ask for more threads than available in the allocation
srun bowtie2 -t  -p 64  -x  $DB -U $READS  >  $RESFILE
# the threads start to compete for the 16 availabe cores.
# If we ask for less threads than available
srun bowtie2 -t  -p 2  -x  $DB -U $READS  >  $RESFILE
# only 2 cores in the allocation  are used
exit
#  Release the allocated resources

sbatch¶

srun and salloc wait/block until resources are available. If the cluster is full you have to wait until resources become available. Sbatch is used to send the job to put the job in the queue and schedule it when the resources become available.

sbatch: create batch file called test.sc in a text editor on the headnode (e.g. vim, nano, gedit). It has the following content:

#!/bin/bash
#SBATCH --cpus-per-task 16
srun bowtie2 -t  -p $SLURM_CPUS_PER_TASK  -x  $DB -U $READS  >  $RESFILE

Note: the #SBATCH is not a comment but sets the cpus per task variable to 16. See below.

Submit the created batch file to slurm using sbatch:

sbatch test.sc

In batch mode the standard output of the issued commands, which would normally appear in the terminal is written to a file. By default the name of this file is slurm-<JOBID>.out After a job is submitted it will run independent of the terminal, you can log out and come back later for the results.

The compute resources that are asked for and other job parameters can be specified by parameters. These parameters can be specified on the commandline (like in the salloc example above). For sbatch the parameters can be included in the batch script. The previously given #SBATCH --cpus-per-task 16 is an example in which 16 cpus are asked for. Lines starting with #SBATCH are interpreted as parameters for the slurm job. Possible parameters can be found in the Slurm documentation: Sbatch

scancel¶

Use scancel to kill running jobs and remove waiting jobs from the queue

scancel [JOBID]

is used to cancel a particular job. All your jobs are killed/removed using:

scancel -u $USER

Slurm Jobs¶

Jobs and Job Steps¶

A compute job can consist of several steps. For example: you download the files, you down sample and then you do an alignment. These steps are job steps and are invoked by srun from with in the batch script. Each srun command in a batch script can ask for its own set of resources as long as it fits in the allocation. In other words the srun commands in a batch script are bounded by the allocation for the batch script. For example an srun can not ask for more cpu’s than asked for in the sbatch file.
Some useful srun flags are:
- -c, --cpus-per-task=ncpus
  
  number of cpus required per task
- -n, --ntasks=ntasks
  
  number of tasks to run
- -N, --nodes=N
  
  number of nodes on which to run (N = min[-max])
- -o, --output=out
  
  location of stdout redirection
- -w, --nodelist=hosts
  
  request a specific list of hosts
Print the name of the node with the command hostname

srun hostname
#omics-cn004  this job was allocated to cn004

Carry out this task four times:

srun -n4 hostname
#omics-cn004
#omics-cn004
#omics-cn004
#omics-cn004  again allocation on cn004

Note that the effect of the -n4 flag is that the program (hostname) is automatically started 4 times. For software which runs on multiple nodes and commucates between instances through mpi this is useful. In case of a multi threaded program it is usually not what you want.

Now, ask for four nodes

srun -N4 hostname
#omics-cn002
#omics-cn003
#omics-cn001
#omics-cn004  now, cn001, cn002, cn003 and cn004 are allocated

Ask for a specific host:

srun -n2 -w omics-cn002 hostname
#omics-cn002
#omics-cn002

Output to a file (here: hn.txt that is stored in your current directory)

srun -N3 -n5 -o hn.txt hostname
cat hn.txt
#omics-cn001
#omics-cn001
#omics-cn002
#omics-cn002
#omics-cn003  #001 and 002 are used twice, 003 is used once

A job consists in two parts: resource requests and job steps. Resource requests consist in a number of CPUs, computing expected duration, amounts of RAM or disk space, etc. Job steps describe tasks that must be done, software which must be run.
The typical way of creating a job is to write a submission script. A submission script is a shell script, e.g. a Bash script, whose comments, if they are prefixed with SBATCH, are understood by Slurm as parameters describing resource requests and other submissions options. You can get the complete list of parameters from the sbatch manpage man sbatch.
get hints for writing a job script at the script generator wizzard Script Generator Wizzard Ceci Ignore the cluster names and replace #SBATCH --partition=defq with #SBATCH --partition=all).

Batch jobs: sbatch¶

Make a text file with the content as in the box below and save it as batch1.sh:
- it writes the output to the file res.txt
- it consists of one task
- it allocates 10 minutes of compute time and 10 MB memory
- I expect this script to run at least 15 seconds and to print 3 hostnames and 3 dates.
- I can execute the script with sbatch sbatch1.sh and monitor the script with squeue

#!/bin/bash
#
#SBATCH --job-name=batch1
#SBATCH --output=res.txt
#
#SBATCH --ntasks=1
#SBATCH --time=10:00
#SBATCH --mem-per-cpu=10
#
srun hostname
srun sleep 5
srun date
srun hostname
srun sleep 5
srun date
srun hostname
srun sleep 5
srun date

execute the script and monitor it

sbatch batch1.sh
squeue

This is the output:
- The job goes through the PENDING state (PD), then enters the RUNNING state (R) and finally goes to the COMPLETED state, or FAILED state.
- Indeed 3 times hostname and 3 times date some 7 seconds apart
- The job id issued was 4963

Parallel Jobs¶

Here, we will only discuss parallel jobs
- by running several instances of a single-threaded program (so-called embarrassingly parallel paradigm or a job array)
- by running a multithreaded program (shared memory paradigm, e.g. with OpenMP or pthreads)
Other types of parallel jobs: see Ceci - see Going parallel.
From this same website: Tasks are requested/created with the –ntasks option, while CPUs, for the multithreaded programs, are requested with the –cpus-per-task option. Tasks can be split across several compute nodes, so requesting several CPUs with the –cpus-per-task option will ensure all CPUs are allocated on the same compute node. By contrast, requesting the same amount of CPUs with the –ntasks option may lead to several CPUs being allocated on several, distinct compute nodes.
- Multithreaded programs run on one specific compute node: use the –cpus-per-task flag with these programs.

Multithreaded bowtie2 Example¶

Many genomics software use a multithreaded approach. We start with a bowtie2 example:
- We want to align 2 fastq files from the European Nucleotide Archive to the Mycoplasma G37 genome.
- Workflow:
  
  download the G37 genome to the /scratch directory of the node
  
  build the genome index on this /scratch directory
  
  download the fastq files to scratch
  
  do the alignment
  
  store the resulting sam files in your personal directory.
- Our batch script is below (save it as align_Mycoplasma and run it with sbatch align_Mycoplasma and monitor it with squeue):

#!/bin/bash
#
#SBATCH --job-name=align_Mycoplasma
#SBATCH --output=res_alignjob.txt
#
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --time=10:00
#SBATCH --mem-per-cpu=2000
#
cd /scratch
srun wget https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/027/325/GCF_000027325.1_ASM2732v1/GCF_000027325.1_ASM2732v1_genomic.fna.gz -P ./
srun bowtie2-build GCF_000027325.1_ASM2732v1_genomic.fna.gz MG37
srun wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR486/ERR486827/ERR486827_1.fastq.gz -P ./
srun wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR486/ERR486827/ERR486827_2.fastq.gz -P ./
srun bowtie2 -x MG37 -1 ERR486827_1.fastq.gz -2 ERR486827_2.fastq.gz --very-fast -p $SLURM_CPUS_PER_TASK -S /zfs/omics/personal/${USER}/result.sam

the output of the job (res_alignjob.txt) is stored where you execute the sbatch. It contains the information that is normally written to your standard output (your screen). In this case, the progress of the download, the progress of the indexing and the alignment summary.
the actual result of the alignment (the sam file) is written to your personal directory.
the number of threads in the bowtie2 command (job step) is taken from the SLURM variable $SLURM_CPUS_PER_TASK that was given at the start of the job. You could have given any number up to 8 with the -p flag. When you issue a number >8 the job will still be executed with the number of threads defined by $SLURM_CPUS_PER_TASK (in this case 8).

Multithreaded Example in C¶

The example below is a C code illustration of how treads are forked from a master thread. This idea can be used when you make your own parallelized code.
Save the file below as omp_hoi.c:

/******************************************************************************
* FILE: omp_hoi.c
* DESCRIPTION:
*   OpenMP Example - Hello World - C/C++ Version
*   In this simple example, the master thread forks a parallel region.
*   All threads in the team obtain their unique thread number and print it.
*   The master thread only prints the total number of threads.  Two OpenMP
*   library routines are used to obtain the number of threads and each
*   thread's number.
* AUTHOR: Blaise Barney  5/99
* LAST REVISED: 04/06/05
******************************************************************************/
#include <omp.h>
#include <stdio.h>
#include <stdlib.h>
#
int main (int argc, char *argv[])
{
int nthreads, tid;
/* Fork a team of threads giving them their own copies of variables */
#pragma omp parallel private(nthreads, tid)
{
        /* Obtain thread number */
        tid = omp_get_thread_num();
        printf("Hello World from Crunchomics thread = %d\n", tid);
        /* Only master thread does this */
        if (tid == 0)
        {
                nthreads = omp_get_num_threads();
                printf("Master thread says: number of threads = %d\n", nthreads);
        }
  }  /* All threads join master thread and disband */
}

and compile it:

gcc -fopenmp omp_hoi.c -o hoi.omp

run it through the following sbatch script:

#!/bin/bash
#SBATCH --job-name=test_omp
#SBATCH --output=res_omp.txt
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=5
export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK
./hoi.omp

Job Arrays: Parallel example with Rscript¶

You can use the sbatch --array=<indexes> parameter to submit a job array, i.e., multiple jobs to be executed with identical parameters. The indexes specification identifies what array index values should be used.
Suppose we study 9 chromosomes of a certain organism (for which we have 9 files, 9 R-objects or something of the kind that we want to process).
Below a sbatch script is shown that spawns 9 sub-tasks (the array starting with 0 to 8). With each sub-task an R script is run that uses the element of the vector ${CHROMS} as indicated by the index of the array. Thus, 9 Rscript-processes are run, each for a chromosome:
copy the code below in a file called parJobBatch.sh

#!/bin/bash
#
#SBATCH --job-name=ParTest
#SBATCH --ntasks=1
#SBATCH --array=0-8
#
CHROMS=("chr1" "chr2" "chr3" "chr4" "chr5" "chr6" "chrX" "chrY" "Mit")
#
srun Rscript parJob.R ${CHROMS[$SLURM_ARRAY_TASK_ID]}

copy the code below in a file called parJob.R

args=commandArgs(trailingOnly=TRUE)
chromosome=args[1]
#
cat("Start work on",chromosome,"\n")
cat("working ....\n")
# put the code what you want to do with each chromosome here
Sys.sleep(20)
cat("Done work on",chromosome,"\n")

The job is started with sbatch parJobBatch.sh
The job 9 job steps each have an entry in the queue

Result:
- 9 files each outputted by a different R process. With content such as:

cat slurm_[jobid]_0.txt

Where the number after the underscore is the jobstep number:

Start work on chr1
working ....
Done work on chr1

Rscript was used here to illustrate the --array functionality, any program which has to be run for a number of inputs can be set up in this way.
A list of files can be used instead of chromosomes. If there are 20 file to be processed the relevant part of the batch script would look something like:

#SBATCH --array=0-19
FILES=(/zfs/omics/personal/*)
srun program ${FILES[$SLURM_ARRAY_TASK_ID]}

Use Conda Environments on the Compute Nodes¶

Run the flye assembly (see 6.2.1) on a compute node using sbatch.
Remark: before you execute the sbatch command, activate the proper conda environmnent. In this case it is necessary to activate nptools because flye was installed in this environment. activate conda nptools The activation will be passed to the compute nodes.

#!/bin/bash
#
#SBATCH --job-name=ecoli_assemble
#SBATCH --output=res_ecoli_assembly.txt
#
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --time=20:00
#SBATCH --mem-per-cpu=32000
#
SCRATCH=/scratch/$USER/ecoli
mkdir -m 700 -p $SCRATCH
cd $SCRATCH
srun wget https://zenodo.org/record/1172816/files/Loman_E.coli_MAP006-1_2D_50x.fasta
date
srun flye --nano-raw Loman_E.coli_MAP006-1_2D_50x.fasta --out-dir /zfs/omics/personal/${USER}/ecoli-batch --threads     $SLURM_CPUS_PER_TASK
date

Save this file as assemble_ecoli.sh and run from the headnode sbatch assemble_ecoli.sh

Interactive Shells Continued¶

For interactive work: use the head node.
In some cases it might be convenient to have shell access on the compute node, for instance to look at the memory and cpu allocation of a specific process.
- limit the duration of this shell by issueing the -t <min>
- use the -w flag to go to the node you want your shell to live in.
- have a shell on cn001 for 1 minute:

hostname
#omics-h0.science.uva.nl
srun -n 1  -t 1 --pty -w omics-cn001 bash -i
hostname
#omics-cn001

Example using an interactive shell¶

Re-run the Multithreaded bowtie2 example. Configure to use 2 threads. Use squeue to find out the node on which the job runs. Then, from the head node (a shell for a minute on -in this case- cn001):

sbatch
srun -n 1  -t 1 --pty -w omics-cn001 bash -i

I see with squeue that my alignment script is running as slurm job 7094 on compute node cn001. Hence, I start an interactive shell at compute node 001 (for a minute) and monitor with top that indeed there is a processor load of 200% (2 threads) used by the bowtie2-align-s program.

Using Slurm Indirectly¶

Using Slurm indirectly¶

Instead of controlling job preparation and submission directly via the command line it is possible to access slurm through packages in (programming) environments. A number of options are presented.

Slurm in R¶

Using the rslurm package an R session running on the headnode (omics-h0) that can run jobs on the cluster. The functions slurm_call and slurm_apply can be used to submit computationally expensive jobs on the compute nodes. The help provided in the package about the functions gives an example. To test demonstrate it on Crunchomics with some slight modifications

require("rslurm")
# Create a data frame of mean/sd values for normal distributions
pars <- data.frame(par_m = seq(-10, 10, length.out = 1000),
           par_sd = seq(0.1, 10, length.out = 1000))
#
# Create a function to parallelize
ftest <- function(par_m, par_sd) {
samp <- rnorm(2e6, par_m, par_sd)
c(s_m = mean(samp), s_sd = sd(samp))
}
#
sjob1 <- slurm_apply(ftest, pars,slurm_options=list(mem="1G"))
get_job_status(sjob1)
# wait until get_job_status(sjob1)$completed
# the job will take about a minute if resources are available.
res <- get_slurm_out(sjob1, "table")
all.equal(pars, res) # Confirm correct output
cleanup_files(sjob1)

Some notes while running it on Crunchomics:

While running jobs the package creates a map in the current directory which is used to communicate data to the R processes on the nodes called slurm_[jobname]. The working directory changes to that directory so relative file paths in the function will not work.
The cleanup_files function can be used to remove this directory.
By default the jobs will have 100MB of memory. Rather than giving up, the typical behaviour of R is to become very very slow if it does not have enough memory. Use slurm_options=list(mem="1G") in the slurm_call/slurm_apply to get enough(Change 1G to the needed amount) memory for your function.
R has to run on the Slurm-cluster (Jobs can not be submitted from omics-app01)

Slurm in Python¶

A package called simple_slurm is available in the madpy3 environment. Check Simple Slurm pypi.org for package documentation and examples.

Slurm and Snakemake¶

Existing snakemake pipelines can be run on the cluster (almost) without change, using a parameter file (cluster.json) snakemake is told how jobs have to be submitted. Below is an example setup of a normal snakefile which maps a number of samples to a genome. This demonstration can be copied and run using the following commands:

cp -r /zfs/omics/software/doc/Crunchomics_intro/SnakeDemo .
cd SnakeDemo
ls
# 3 files and an empty directory are copied
#
# Snakemake has to be available use
# source /zfs/omics/software/v_envs/madpy3/bin/activate
# if which snakemake comes back without result
#
batch snakemake.cmd

Check the snakefile using

cat Snakefile

Snakefile is a typical snakemake input file to trim and map some samples, but the process should work for any Snakefile. The snakefile can be processed unchanged by snakemake utilizing the cluster using the a parameter file:

#cluster.json
{
"__default__" :
{
        "time" : "00:30:00",
        "n" : 1,
        "c" : 1,
        "memory"    : "2G",
        "partition" : "all",
        "output"    : "logs/slurm-%A_{rule}.{wildcards}.out",
        "error"     : "logs/slurm-%A_{rule}.{wildcards}.err",
},
"align_to_database" :
{
        "time" : "02:00:00",
        "memory"    : "10G",
        "c" : 8
},
"trim_read" :
{
        "time" : "01:00:00",
        "c" : 2
}
}

In the parameter file the options values can be specified for how snakemake has to configure the slurm jobs. In it the number of cores, the amount of memory etc. is given for the rules. If the name of the rule is not given snakemake uses the values given in __default__

Using the --cluster and --cluster-config parameters snakemake is able replace the job execution commands with slurm batch jobs.

snakemake -j 8 --cluster-config cluster.json --cluster "sbatch  -p {cluster.partition} -n {cluster.n}  -c {cluster.c} -t {cluster.time} --mem {cluster.memory} -o {cluster.output} "

The command above can be wrapped into an sbatch script (the file: snakemake.cmd) In this way the snakemake program itself runs as a slurm job. Note that sbatch commands executed from within an sbatch job will run in a new and separate allocation independent of the allocation from which sbatch is run. While the snakemake program itself runs on a single core the batches it runs like the mapping tasks can use 8 cores.

The mapping of parameters defined in the cluster.json file to the sbatch command. Snakemake takes care that jobs are submitted using the parameters in the cluster.json file. By default the parameters in __default__ are used. For rules that match a clause in the cluster.json file for example align_to_database changes from the default can be included for example the the number of cores (8 instead of 1) and the time (2 hour intead of 30 minutes). As mentioned in the introduction snakefiles can be run almost unchanged. Some rules do very little actual work and it make little sense to setup and schedule a job for it for example creating a directory. These rules can be declared as localrules at the top of the snakefile. This means snakemake will execute them directly instead of submitting them as a slurm job. Also depending on if and how thread counts are dealt with in the snakefile tweaking can improve the utilization of the allocated resources.

The snakemake parameter -j which normally limits the number of cores used by snakemake limits the number of concurrently submitted jobs regardless of the number of cores each job allocates. Setting -j to high values such as 999 as is done in some examples on the internet is usually not a good idea as it might claim the complete cluster.

Using snakemake in combination with the (super fast NVMe SSD) local scratch partition for temporary storage adds an extra challenge. The main mode of operation of snakemake is checking the existence of files. However, a snakemake process running on the headnode can not access the scratch on the nodes. A possibility is to configure snakemake (in cluster.json) to run on a single node and to combine that with running snakemake itself using sbatch in the selected node.

Debugging Slurm jobs¶

If things do not run as expected there are a number of tools and tricks to find out why.

Initially squeue can be used to get the state of the job. Is it in the queue, running or ended. A job which request resources which are not available is queued until the resources become available. If a jobs ask for 6 nodes it will be queued but never run as there are only 5 compute nodes.
Check the output file of the batchjob (slurm_[jobid].out or the name given using –output parameter). Use cat slurm_[jobid].out or tail slurm_[jobid].out to check what the job sent to its standard output.
To see if the job runs as expected, an additional interactive job can be started on the node the job is running on using srun -w [nodeX] --pty bash -i. The commands top and htop are useful to see which programs are running and if the program is using the allocated resources as expected.
If it is not possible to allocate a job on the node an option is to directly ssh into node. This is only possible if you have an active job on the particular node.
A common problem is lack of memory. Memory is also limited in by the slurm allocation. The default memory allocation is 100MB. While this is fine for some, it is not enough for many jobs. Some jobs simply give up with an error if they don’t have enough memory. Others try to make the best of it. This might result in jobs, which are expected to complete in a couple of hours to run for days. A telltale sign of this happening is that memory usage shown by top is close to or equal to the allocation asked for. While setting up jobs keep in mind the compute nodes have 512GB each. That is 8G for each of the 64 cores on average. Don’t allocate what you do not need: this might be used by others. It is not unreasonable to allocate 128GB of memory for a 16-core job.

Use of Local NVMe Storage on Compute Nodes¶

Each compute node in the cluster has 7.3TB local and very fast NVMe storage available in /scratch. By storing temporary and/or much accessed data on the scratch disk, file access times can be improved significantly. It also reduces the load on the filer which will benefit all users. However as local storage it can be only accessed on a particular node. So either data has to be synchronized over all nodes, or jobs have to be fixed on particular nodes using the -w flag

You might remember that in the Multithreaded bowtie2 example we used the local /scratch disk to copy the genome index and the fastq files to. The alignment job was calculated (in this particular case on cn001).
- These files are not automatically removed when the job has finished. So multiple jobs can access and further process data in scratch.
- To prevent /scratch from filling up, files which have not been accessed (read or written) for (currently) a month are removed. The time unused files are removed might be reduced if the file system fills up too quickely.
- This policy makes it possible to reuse large files on the local /scratch disk on a compute node. For instance, look at the scratch of cn001:

srun -n1 -w omics-cn001 ls -lh /scratch

Here we make use of the genome index on cn001 while doing an alignment with 2 new files (save the file below as align_Mycoplasma3.sh):

#!/bin/bash
#
#SBATCH --job-name=align_Mycoplasma
#SBATCH --output=res_alignjob.txt
#
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --time=10:00
#SBATCH --mem-per-cpu=2000
#
WD="/scratch/$USER"
mkdir -p $WD
cd $WD
#
#test if the Mycoplasma genome is already there, download it and build the index if not.
fl="GCF_000027325.1_ASM2732v1_genomic.fna.gz"
if [ ! -f "${fl}" ]; then
        srun wget https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/027/325/GCF_000027325.1_ASM2732v1/GCF_000027325.1_ASM2732v1_genomic.fna.gz -P ./
        srun bowtie2-build GCF_000027325.1_ASM2732v1_genomic.fna.gz MG37
fi
#
#only download the 2 illumina files if they are not there
fl="ERR486828_1.fastq.gz"
if [ ! -f "${fl}" ]; then
        srun wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR486/ERR486828/ERR486828_1.fastq.gz -P ./
fi
#
fl="ERR486828_2.fastq.gz"
if [ ! -f "${fl}" ]; then
        srun wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR486/ERR486828/ERR486828_2.fastq.gz -P ./
fi
#
date
srun bowtie2 -x MG37 -1 ERR486828_1.fastq.gz -2 ERR486828_2.fastq.gz --very-fast -p $SLURM_CPUS_PER_TASK -S /zfs/omics/personal/${USER}/result_ERR486828.sam
date

execute with sbatch -w omics-cn001 align_Mycoplasma3.sh
from tail -n21 res_alignjob.txt:

I conclude the script succeeded.
look at the /scratch directory:

srun -n1 -w omics-cn001 ls -lh /scratch

Indeed, the index MG37 is reused (timestamp 12:00) and the two fastq files ending on 28 are newly downloaded (timestamp 18:07).
Obviously, this way of working is especially useful if there is a lot of IO. The size of the local scratch is net 7.3 TB.

srun -n1 -w omics-cn001 df -h /scratch

Docker / Singularity Containers¶

Containers are software environments which make it possible to distrubute applications along with all the dependencies needed and run these on all computers which have the container engine installed. Docker is a well know container platform. For security reasons the Docker engine is not available on Omics. Singularity is an alternative to Docker which is better suited for running in a multi-user plaform such as Crunchomics. Singularity is able to deal with Docker containers and many run without problems.

Singularity Hub¶

singularity pull singularity-images.sif shub://vsoch/singularity-images
singularity run lolcow.sif
singularity run lolcow.sif

Docker Hub¶

singularity build megahit docker://vout/megahit
cat /etc/*release
#CentOS Linux release 7.8.2003 (Core)
singularity shell megahit
#DISTRIB_ID=Ubuntu
#DISTRIB_RELEASE=18.04
#DISTRIB_CODENAME=bionic
exit
rm -rf ~/assembly
singularity run megahit -v
singularity run megahit -1 ERR486840_1.fastq.gz -2 ERR486840_2.fastq.gz -o ./assembly
awk '/^>/ {print}' assembly/final.contigs.fa | wc -l
#22

#Run this in a SLURM batch job:
sbatch batch_megahit.txt

#!/bin/bash
#SBATCH --job-name=assembly_gen.        # Job name

echo "Running megahit"

cd # go to home

rm -rf assembly

singularity run megahit -t 8 -1 ERR486840_1.fastq.gz -2 ERR486840_2.fastq.gz -o ./assembly
awk '/^>/ {print}' assembly/final.contigs.fa | wc -l

The Crunchomics’ Documentation¶

Crunchomics: The Genomics Compute Environment for SILS and IBED¶

Log in on the Head Node¶

On Windows¶

Login Without a Password with RSA Keys¶

On Linux and Mac¶

Login Without a Password with RSA Keys¶

Preparing Your Account¶

Getting Your Environment Ready¶

DIY¶

Account¶

The Crunchomics Application Server¶

CLC Genomics Workbench¶

Start RStudio¶

The Crunchomics Compute Cluster¶

Interactive access¶

Example of Interactive R Sessions¶

Miniconda¶

Installation¶

Get miniconda¶

Install Miniconda¶

Installation of a Conda Package¶

Example: Using Conda Installation of Flye¶

Slurm Overview¶

Additional help and information¶

Quick slurm demo¶

Information about the cluster¶

Multithreading¶

srun¶

salloc¶

sbatch¶

scancel¶

Slurm Jobs¶

Jobs and Job Steps¶

Batch jobs: sbatch¶

Parallel Jobs¶

Multithreaded bowtie2 Example¶

Multithreaded Example in C¶

Job Arrays: Parallel example with Rscript¶

Use Conda Environments on the Compute Nodes¶

Interactive Shells Continued¶

Example using an interactive shell¶

Using Slurm Indirectly¶

Using Slurm indirectly¶

Slurm in R¶

Slurm in Python¶

Slurm and Snakemake¶

Debugging Slurm jobs¶

Use of Local NVMe Storage on Compute Nodes¶

Docker / Singularity Containers¶

Singularity Hub¶

Docker Hub¶

Indices and tables¶