I have a large file to analyze using "jellyfish query", which is not multithreaded. I have split the big file into 29 manageable fragments, to run as an array on SLURM. However, these are sitting in the workload queue for ages, whereas if I could request a whole node (32 cpus) they would get in a separate queue with quicker availability. Is there a way to tell SLURM to run the command on these fragments in parallel across all the cpus in a node, instead of as a serial array?
You could ask for 29 tasks, 1 cpu per task (you will get from 29 cpus on a node to 1 cpu in 29 different nodes), and in the slurm script you should start your calculus with srun, telling srun to allocate one task/cpu per chunk.
.
.
.
#SBATCH --ntasks=29
#SBATCH --cpus-per-task=1
.
.
.
for n in {1..29}
do
srun -n 1 <your_script> $n &
done
wait
I suggest running a python script to multithread this for you, then submit a SLURM job to run the python script.
from multiprocessing import Pool
import subprocess
num_threads = 29
def sample_function(input_file):
return subprocess.run(["cat", input_file], check=True).stdout
input_file_list = ['one','two','three']
pool = Pool(processes=num_threads)
[pool.apply_async(sample_function, args=(input_file,)) for input_file in input_file_list]
pool.close()
pool.join()
This assumes you have files "one", "two", and "three". Obviously you need to replace:
the input file list
job you want to run with subprocess
Thanks for the suggestions! I found a much less elegant but still functional way:
#SBATCH --nodes=1
#SBATCH --ntasks=32
#SBATCH --cpus-per-task=1
jellyfish query...fragment 1 &
jellyfish query...fragment 2 &
...
jellyfish query...fragment 29
wait
Related
Let's suppose I have the following bash script (bash.sh) to be run on a HPC using slurm:
#!/bin/bash
#SBATCH --job-name test
#SBATCH --ntasks 4
#SBATCH --time 00-05:00
#SBATCH --output out
#SBATCH --error err
#SBATCH --array=0-24
readarray -t VARS < file.txt
VAR=${VARS[$SLURM_ARRAY_TASK_ID]}
export VAR
bash my_script.sh
This script will run 25 times the my_script.sh script changing variables taken in the file.txt file. In other words, 25 jobs will be launched all together, if I submit bash.sh with the command sbatch bash.sh.
Is there a way I can limit the number of jobs to be ran at the same time (e.g. 5) until all 25 will be completed?
And if there is a way in doing so, how can I do the same but with having 24 jobs in total (i.e. not a number divisible by 5)?
Thanks
Extract from Slurm's sbatch documentation:
-a, --array=<indexes>
... A maximum number of simultaneously running tasks from the job array may be specified using a "%" separator. For example "--array=0-15%4" will limit the number of simultaneously running tasks from this job array to 4. ...
This should limit the number of running jobs to 5 in your array:
#SBATCH --array=0-24%5
I want to perform similar (parallel) runs with job arrays with SLURM, by submitting a unique job. When a single task is finished, I want to start a second run that takes in iput a file produced by the first task. Is it possible? I make an example.
I want to run 3 parallel tasks, with $SLURM_ARRAY_TASK_ID=0,1,2.
When a single task is finished, e.g. srun ./my_program1.exe 0 is finished, I want to start srun ./my_program2.exe 0 < input_from_myprogram1_taskid=0, even if srun ./my_program1.exe 1is still running (each task could have a slighlty different execution time). Is it safe, does it make sense?
#!/bin/bash
#
#SBATCH --job-name=test_emb_arr
#SBATCH --output=res_emb_arr.txt
#
#SBATCH --ntasks=1
#SBATCH --time=10:00
#SBATCH --mem-per-cpu=100
#
#SBATCH --array=0-2
srun ./my_program1.exe $SLURM_ARRAY_TASK_ID
###something that tells to the machine to wait until srun ./my_program1.exe $SLURM_ARRAY_TASK_ID is finished before make the following second run
srun ./my_program2.exe $SLURM_ARRAY_TASK_ID < input_from_previous_single_run
Should not the regular && be your solution here?
https://www.javatpoint.com/linux-double-ampersand
srun ./my_program1.exe $SLURM_ARRAY_TASK_ID && srun ./my_program2.exe $SLURM_ARRAY_TASK_ID < input_from_previous_single_run
Just to clarify, the double && makes the first one finish before starting the next.
The SLURM job array submission isn't working as I expected. When I run my sbatch script to create the array and run the programs I expect it to fully utilize all the cores that are available, however, it only allows one job from the array to run on the a given node at a time. SCONTROL shows the job using all 36 cores on the node when I specified 4 cores for the process. Additionally, I want to restrict the jobs to running on one specific node, however if other nodes are unused, it will submit a job onto them as well, using every core available on that node.
I've tried submitting the jobs by changing the parameters for --nodes, --ntasks, --nodelist, --ntasks-per-node, --cpus-per-task, setting OMP_NUM_THREADS, and specifying the number of cores for mpirun directly. None of these options seemed to change anything at all.
#!/bin/bash
#SBATCH --time=2:00:00 # walltime
#SBATCH --ntasks=1 # number of processor cores (i.e. tasks)
#SBATCH --nodes=1 # number of nodes
#SBATCH --nodelist node001
#SBATCH --ntasks-per-node=9
#SBATCH --cpus-per-task=4
#SBATCH --mem-per-cpu=500MB # memory per CPU core
#SBATCH --array=0-23%8
export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK
mpirun -n 4 MYPROGRAM
I expected to be able to run eight instances of MYPROGRAM, each utilizing four cores for a parallel operation. In total, I expected to use 32 cores at a time for MYPROGRAM, plus however many cores are needed to run the job submission program.
Instead, my squeue output looks like this
JOBID PARTITION NAME USER ST TIME NODES CPUS
num_[1-23%6] any MYPROGRAM user PD 0:00 1 4
num_0 any MYPROGRAM user R 0:14 1 36
It says that I am using all available cores on the node for this process, and will not allow additional array jobs to begin. While MYPROGRAM runs exactly as expected, there is only once instance of it running at any given time.
And my SCONTROL output looks like this:
UserId=user(225589) GroupId=domain users(200513) MCS_label=N/A
Priority=4294900562 Nice=0 Account=(null) QOS=normal
JobState=PENDING Reason=Resources Dependency=(null)
Requeue=1 Restarts=0 BatchFlag=1 Reboot=0 ExitCode=0:0
RunTime=00:00:00 TimeLimit=02:00:00 TimeMin=N/A
SubmitTime=2019-06-21T18:46:25 EligibleTime=2019-06-21T18:46:26
StartTime=Unknown EndTime=Unknown Deadline=N/A
PreemptTime=None SuspendTime=None SecsPreSuspend=0
LastSchedEval=2019-06-21T18:46:28
Partition=any AllocNode:Sid=w***:45277
ReqNodeList=node001 ExcNodeList=(null)
NodeList=(null) SchedNodeList=node001
NumNodes=1-1 NumCPUs=4 NumTasks=1 CPUs/Task=4 ReqB:S:C:T=0:0:*:*
TRES=cpu=4,mem=2000M,node=1
Socks/Node=* NtasksPerN:B:S:C=9:0:*:* CoreSpec=*
MinCPUsNode=36 MinMemoryCPU=500M MinTmpDiskNode=0
Features=(null) DelayBoot=00:00:00
Gres=(null) Reservation=(null)
OverSubscribe=NO Contiguous=0 Licenses=(null) Network=(null)
Power=
JobId=1694 ArrayJobId=1693 ArrayTaskId=0 JobName=launch_vasp.sh
UserId=user(225589) GroupId=domain users(200513) MCS_label=N/A
Priority=4294900562 Nice=0 Account=(null) QOS=normal
JobState=RUNNING Reason=None Dependency=(null)
Requeue=1 Restarts=0 BatchFlag=1 Reboot=0 ExitCode=0:0
RunTime=00:00:10 TimeLimit=02:00:00 TimeMin=N/A
SubmitTime=2019-06-21T18:46:25 EligibleTime=2019-06-21T18:46:26
StartTime=2019-06-21T18:46:26 EndTime=2019-06-21T20:46:26 Deadline=N/A
PreemptTime=None SuspendTime=None SecsPreSuspend=0
LastSchedEval=2019-06-21T18:46:26
Partition=any AllocNode:Sid=w***:45277
ReqNodeList=node001 ExcNodeList=(null)
NodeList=node001
BatchHost=node001
NumNodes=1 NumCPUs=36 NumTasks=1 CPUs/Task=4 ReqB:S:C:T=0:0:*:*
TRES=cpu=36,mem=18000M,node=1,billing=36
Socks/Node=* NtasksPerN:B:S:C=9:0:*:* CoreSpec=*
MinCPUsNode=36 MinMemoryCPU=500M MinTmpDiskNode=0
Features=(null) DelayBoot=00:00:00
Gres=(null) Reservation=(null)
OverSubscribe=NO Contiguous=0 Licenses=(null) Network=(null)
Power=
Something is going wrong in how SLURM is assigning cores to tasks, but nothing I've tried changes anything. I'd appreciate any help you can give.
Check if the slurm.conf file allows consumable resources. The default is to assign nodes exclusively. I had to add the following lines to allow per-score scheduling
SelectType=select/cons_res
SelectTypeParameters=CR_Core
I am using PBS, HPC to submit serially written C codes. I have to run suppose 5 codes in 5 different directories. when I select 1 node and 5 cores select=1:ncpus=5, and submits it with ./submit &. It forks and runs all the 5 jobs. The moment I choose 5 node and 1 cores select=5:ncpus=1, and submits it with ./submit &. Only 1 core of the first node runs all five jobs and rest 4 threads are free, speed decreased to 1/5.
My question is, Is it possible to fork the job between the nodes as well?
because when I select on HPC select=1:ncpus=24 it gets to Que instead select=4:ncpus=6 runs.
Thanks.
You should consider using job arrays (using option #PBS -t 1-5) with I node and 1 cpu each. Then 5 independent jobs will start and your job will wait less in the queue.
Within your script you can use environment variable PBS_ARRAYID to identify the task and use it to set appropriate directory and start the appropriate C code. Something like this:
#!/bin/bash -l
#PBS -N yourjobname
#PBS -q yourqueue
#PBS -l nodes=1:ppn=1
#PBS -t 1-5
./myprog-${PBS_ARRAYID}.c
This script will run 5 jobs and each of them will run programs with a name myprog-*.c where * is a number between 1 and 5.
I have a problem where I need to launch the same script but with different input arguments.
Say I have a script myscript.py -p <par_Val> -i <num_trial>, where I need to consider N different par_values (between x0 and x1) and M trials for each value of par_values.
Each trial of M is such that almost reaches the time limits of the cluster where I am working on (and I don't have priviledges to change this). So in practice I need to run NxM independent jobs.
Because each batch jobs has the same node/cpu configuration, and invokes the same python script, except for changing the input parameters, in principle, in pseudo-language I should have a sbatch script that should do something like:
#!/bin/bash
#SBATCH --job-name=cv_01
#SBATCH --output=cv_analysis_eis-%j.out
#SBATCH --error=cv_analysis_eis-%j.err
#SBATCH --partition=gpu2
#SBATCH --nodes=1
#SBATCH --cpus-per-task=4
for p1 in 0.05 0.075 0.1 0.25 0.5
do
for i in {0..150..5}
do
python myscript.py -p p1 -v i
done
done
where every call of the script is itself a batch job.
Looking at the sbatch doc, the -a --array option seems promising. But in my case I need to change the input parameters for every script of the NxM that I have. How can I do this? I would like not to write NxM batch scripts and then list them in a txt file as suggested by this post. Nor the solution proposed here seems ideal, as this is the case imho of a job array. Moreover I would like to make sure that all the NxM scripts are launched at the same time, and the invoking above script is terminated right after, so that it won't clash with the time limit and my whole job will be terminated by the system and remain incomplete (whereas, since each of the NxM jobs is within such limit, if they are run together in parallel but independent, this won't happen).
The best approach is to use job arrays.
One option is to pass the parameter p1 when submitting the job script, so you will only have one script, but will have to submit it multiple times, once for each p1 value.
The code will be like this (untested):
#!/bin/bash
#SBATCH --job-name=cv_01
#SBATCH --output=cv_analysis_eis-%j-%a.out
#SBATCH --error=cv_analysis_eis-%j-%a.err
#SBATCH --partition=gpu2
#SBATCH --nodes=1
#SBATCH --cpus-per-task=4
#SBATCH -a 0-150:5
python myscript.py -p $1 -v $SLURM_ARRAY_TASK_ID
and you will submit it with:
sbatch my_jobscript.sh 0.05
sbatch my_jobscript.sh 0.075
...
Another approach is to define all the p1 parameters in a bash array and submit NxM jobs (untested)
#!/bin/bash
#SBATCH --job-name=cv_01
#SBATCH --output=cv_analysis_eis-%j-%a.out
#SBATCH --error=cv_analysis_eis-%j-%a.err
#SBATCH --partition=gpu2
#SBATCH --nodes=1
#SBATCH --cpus-per-task=4
#Make the array NxM
#SBATCH -a 0-150
PARRAY=(0.05 0.075 0.1 0.25 0.5)
#p1 is the element of the array found with ARRAY_ID mod P_ARRAY_LENGTH
p1=${PARRAY[`expr $SLURM_ARRAY_TASK_ID % ${#PARRAY[#]}`]}
#v is the integer division of the ARRAY_ID by the lenght of
v=`expr $SLURM_ARRAY_TASK_ID / ${#PARRAY[#]}`
python myscript.py -p $p1 -v $v
If you use SLURM job arrays, you could linearise the index of your two for loops, and then do a comparison of the loop index and the array task id:
#!/bin/bash
#SBATCH --job-name=cv_01
#SBATCH --output=cv_analysis_eis-%j.out
#SBATCH --error=cv_analysis_eis-%j.err
#SBATCH --partition=gpu2
#SBATCH --nodes=1
#SBATCH --cpus-per-task=4
#SBATCH -a 0-154
# NxM = 5 * 31 = 154
p1_arr=(0.05 0.075 0.1 0.25 0.5)
# SLURM_ARRAY_TASK_ID=154 # comment in for testing
for ip1 in {0..4} # 5 steps
do
for i in {0..150..5} # 31 steps
do
let task_id=$i/5+31*$ip1
# printf $task_id"\n" # comment in for testing
if [ "$task_id" -eq "$SLURM_ARRAY_TASK_ID" ]
then
p1=${p1_arr[ip1]}
# printf "python myscript.py -p $p1 -v $i\n" # comment in for testing
python myscript.py -p $p1 -v $i\n
fi
done
done
This answer is pretty similar to Carles. I would thus have preferred to write it as a comment but do not have enough reputation.
According to this page, job arrays incur significant overhead:
If the running time of your program is small, say ten minutes or less, creating a job array will incur a lot of overhead and you should consider packing your jobs.
That page provides a few examples to run your kind of job, using both arrays and "packed jobs."
If you don't want/need to specify the resources for your job, here is another approach: I'm not sure if it's a usecase that was intended by Slurm, but it appears to work, and the submission script looks a little bit nicer since we don't have to linearize the indices to fit it into the job-array paradigm. Plus it works well with nested loops of arbitrary depth.
Run this directly as a shell script:
#!/bin/bash
FLAGS="--ntasks=1 --cpus-per-task=1"
for i in 1 2 3 4 5; do
for j in 1 2 3 4 5; do
for k in 1 2 3 4 5; do
sbatch $FLAGS testscript.py $i $j $k
done
done
done
where you need to make sure testscript.py points to the correct interpreter in the first line using the #! e.g.
#!/usr/bin/env python
import time
import sys
time.sleep(5)
print "This is my script"
print sys.argv[1], sys.argv[2], sys.argv[3]
Alternatively (untested), you can use the --wrap flag like this
sbatch $FLAGS --wrap="python testscript.py $i $j $k"
and you won't need the #!/usr/bin/env python line in testscript.py