Hello to the community:
I recently started to use ORCA software for some quantum calculation but I have been having a lot of problems to lunch a parallel calculation in the cluster of my University.
To install Orca I used the static version:
orca_4_2_1_linux_x86-64_openmpi314.tar.xz.
In a shared direction of the cluster (/data/shared/opt/ORCA/).
And putted in my ~/.bash_profile:
export PATH="/data/shared/opt/ORCA/orca_4_2_1_linux_x86-64_openmpi314:$PATH"
export LD_LIBRARY_PATH="/data/shared/opt/ORCA/orca_4_2_1_linux_x86-64_openmpi314:$LD_LIBRARY_PATH"
For the installation of the corresponding OpenMPI version (3.1.4)
tar -xvf openmpi-3.1.4.tar.gz
cd openmpi-3.1.4
./configure --prefix="/data/shared/opt/ORCA/openmpi314/"
make -j 10
make install
When I use the frontend server all is wonderful:
With a .sh like this:
#! /bin/bash
export PATH="/data/shared/opt/ORCA/openmpi314/bin:$PATH"
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/data/shared/opt/ORCA/openmpi314/lib"
$(which orca) test.inp > test.out
and an input like this:
# Computation of myjob at b3lyp/6-31+G(d,p)
%pal nprocs 10 end
%maxcore 8192
! RKS B3LYP 6-31+G(d,p)
! TightSCF Grid5 NoFinalGrid
! Opt
! Freq
%cpcm
smd true
SMDsolvent "water"
end
* xyz 0 1
C 0 0 0
O 0 0 1.5
*
The problem appears when I use the nodes:
.inp file:
#! Computation at RKS B3LYP/6-31+G(d,p) for cis1_bh267_m_Cell_152
%pal nprocs 12 end
%maxcore 8192
! RKS B3LYP 6-31+G(d,p)
! TightSCF Grid5 NoFinalGrid
! Opt
! Freq
%cpcm
smd true
SMDsolvent "water"
end
* xyz 0 1
C -4.38728130 0.21799058 0.17853303
C -3.02072869 0.82609890 -0.29733316
F -2.96869122 2.10937041 0.07179384
F -3.01136328 0.87651596 -1.63230798
C -1.82118365 0.05327804 0.23420220
O -2.26240947 -0.92805650 1.01540713
C -0.53557484 0.33394113 -0.05236121
C 0.54692198 -0.46942807 0.50027196
O 0.31128292 -1.43114232 1.22440290
C 1.93990391 -0.12927675 0.16510948
C 2.87355011 -1.15536140 -0.00858832
C 4.18738231 -0.82592189 -0.32880964
C 4.53045856 0.52514329 -0.45102225
N 3.63662927 1.52101319 -0.26705841
C 2.36381718 1.20228695 0.03146190
F -4.51788749 0.24084604 1.49796862
F -4.53935644 -1.04617745 -0.19111502
F -5.43718443 0.87033190 -0.30564680
H -1.46980819 -1.48461498 1.39034280
H -0.26291843 1.15748249 -0.71875720
H 2.57132559 -2.20300864 0.10283592
H 4.93858460 -1.60267627 -0.48060140
H 5.55483009 0.83859415 -0.70271364
H 1.67507560 2.05019549 0.17738396
*
.sh file (Slurm job):
#!/bin/bash
#SBATCH -p deflt #which partition I want
#SBATCH -o cis1_bh267_m_Cell_152_myjob.out #path for the slurm output
#SBATCH -e cis1_bh267_m_Cell_152_myjob.err #path for the slurm error output
#SBATCH -c 12 #number of cpu(logical cores)/task (task is normally an MPI process, default is one and the option to change it is -n)
#SBATCH -t 2-00:00 #how many time I want the resources (this impacts the job priority as well)
#SBATCH --job-name=cis1_bh267_m_Cell_152 #(to recognize your jobs when checking them with "squeue -u USERID")
#SBATCH -N 1 #number of node, usually 1 when no parallelization over nodes
#SBATCH --nice=0 #lowering your priority if >0
#SBATCH --gpus=0 #number of gpu you want
# This block is echoing some SLURM variables
echo "Jobid = $SLURM_JOBID"
echo "Host = $SLURM_JOB_NODELIST"
echo "Jobname = $SLURM_JOB_NAME"
echo "Subcwd = $SLURM_SUBMIT_DIR"
echo "SLURM_CPUS_PER_TASK = $SLURM_CPUS_PER_TASK"
# This block is for the execution of the program
export PATH="/data/shared/opt/ORCA/openmpi314/bin:$PATH"
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/data/shared/opt/ORCA/openmpi314/lib"
$(which orca) ${SLURM_JOB_NAME}.inp > ${SLURM_JOB_NAME}.log --use-hwthread-cpus
I used the --use-hwthread-cpus flag as a recommendation but the same problem appears with and without this flag.
All the error is:
There are not enough slots available in the system to satisfy the 12 slots that were requested by the application: /data/shared/opt/ORCA/orca_4_2_1_linux_x86-64_openmpi314/orca_gtoint_mpi
Either request fewer slots for your application, or make more slots available for use. A "slot" is the Open MPI term for an allocatable unit where we can launch a process. The number of slots available are defined by the environment in which Open MPI processes are run:
1. Hostfile, via "slots=N" clauses (N defaults to number of processor cores if not provided)
2. The --host command line parameter, via a ":N" suffix on the hostname (N defaults to 1 if not provided)
3. Resource manager (e.g., SLURM, PBS/Torque, LSF, etc.)
4. If none of a hostfile, the --host command line parameter, or an RM is present, Open MPI defaults to the number of processor cores In all the above cases, if you want Open MPI to default to the number
of hardware threads instead of the number of processor cores, use the --use-hwthread-cpus option.
Alternatively, you can use the --oversubscribe option to ignore the number of available slots when deciding the number of processes to launch.
*[file orca_tools/qcmsg.cpp, line 458]:
.... aborting the run*
When I go to the output of the calculation, it looks like start to run but when launch the parallel jobs fail and give:
ORCA finished by error termination in GTOInt
Calling Command: mpirun -np 12 --use-hwthread-cpus /data/shared/opt/ORCA/orca_4_2_1_linux_x86-64_openmpi314/orca_gtoint_mpi cis1_bh267_m_Cell_448.int.tmp cis1_bh267_m_Cell_448
[file orca_tools/qcmsg.cpp, line 458]:
.... aborting the run
We have two kind of nodes on the cluster:
A punch of them are:
Xeon 6-core E-2136 # 3.30GHz (12 logical cores) and Nvidia GTX 1070Ti
And the other ones:
AMD Epyc 24-core (24 logical cores) and 4x Nvidia RTX 2080Ti
Using the command scontrol show node the details of one node of each group are:
First Group:
NodeName=fang1 Arch=x86_64 CoresPerSocket=6
CPUAlloc=12 CPUTot=12 CPULoad=12.00
AvailableFeatures=(null)
ActiveFeatures=(null)
Gres=gpu:gtx1070ti:1
NodeAddr=fang1 NodeHostName=fang1 Version=19.05.5
OS=Linux 5.7.12-arch1-1 #1 SMP PREEMPT Fri, 31 Jul 2020 17:38:22 +0000
RealMemory=15923 AllocMem=0 FreeMem=171 Sockets=1 Boards=1
State=ALLOCATED ThreadsPerCore=2 TmpDisk=7961 Weight=1 Owner=N/A MCS_label=N/A
Partitions=deflt,debug,long
BootTime=2020-10-27T09:56:18 SlurmdStartTime=2020-10-27T15:33:51
CfgTRES=cpu=12,mem=15923M,billing=12,gres/gpu=1,gres/gpu:gtx1070ti=1
AllocTRES=cpu=12,gres/gpu=1,gres/gpu:gtx1070ti=1
CapWatts=n/a
CurrentWatts=0 AveWatts=0
ExtSensorsJoules=n/s ExtSensorsWatts=0 ExtSensorsTemp=n/s
Second Group
NodeName=fang50 Arch=x86_64 CoresPerSocket=24
CPUAlloc=48 CPUTot=48 CPULoad=48.00
AvailableFeatures=(null)
ActiveFeatures=(null)
Gres=gpu:rtx2080ti:4
NodeAddr=fang50 NodeHostName=fang50 Version=19.05.5
OS=Linux 5.7.12-arch1-1 #1 SMP PREEMPT Fri, 31 Jul 2020 17:38:22 +0000
RealMemory=64245 AllocMem=0 FreeMem=807 Sockets=1 Boards=1
State=ALLOCATED ThreadsPerCore=2 TmpDisk=32122 Weight=1 Owner=N/A MCS_label=N/A
Partitions=deflt,long
BootTime=2020-12-15T10:09:43 SlurmdStartTime=2020-12-15T10:14:17
CfgTRES=cpu=48,mem=64245M,billing=48,gres/gpu=4,gres/gpu:rtx2080ti=4
AllocTRES=cpu=48,gres/gpu=4,gres/gpu:rtx2080ti=4
CapWatts=n/a
CurrentWatts=0 AveWatts=0
ExtSensorsJoules=n/s ExtSensorsWatts=0 ExtSensorsTemp=n/s
I use in the script of Slurm the flag -c, --cpus-per-task = integer; and in the input for Orca the command %pal nprocs integer end. I tested different combinations of this two parameters in order to see if I am using more CPU than the available:
-c, --cpus-per-task = integer
%pal nprocs integer end
None
6
None
3
None
2
1
2
1
12
2
6
3
4
12
12
With different amount of memories: 8000 MBi and 2000 MBi (my total memory is around 15 GBi). And in all the cases the same error appears. I am not an expert user neither in ORCA non in informatic (but maybe you guess this for the extension of the question), so maybe the solution is simple but I really don’t have it, Idon't know what's going on!
A lot of thanks in advance,
Alejandro.
Faced the same issue.
Explicit declaration --prefix ${OMPI_HOME} directly as ORCA parameter and using of static linked ORCA version helps me:
export RSH_COMMAND="/usr/bin/ssh"
export PARAMS="--mca routed direct --oversubscribe -machinefile ${HOSTS_FILE} --prefix ${OMPI_HOME}"
$ORCA_DIR/orca $WORKDIR/$JOBFILE.inp "$PARAMS" > $WORKDIR/$JOBFILE.out
Also, It's better to build OpenMPI 3.1.x with --disable-builtin-atomics flag.
Thank you #Alexey for your answer. And sorry for the wrong Tag, like I said, I am pretty rookie on this stuff.
The problem was not in the Orca or OpenMPI configuration but in the bash script used for scheduled the Slurm job.
I thought that the entire Orca job itself was what Slurm call a "task". For that reason I declared the flag --cpus-per-task equal to the number of parallel jobs that I want to do with Orca. But the problem is that each parallel Orca job (that is launch using OpenMPI) is a task for Slurm. Therefore with my Slurm script I was reserving a node with at least 12 CPU, but when Orca launch their parallel jobs, each one ask for 12 CPU, so: "There are not enough slots available ..." because I needed 144 CPU.
The rest of the cases in the table of my Question fails for another reason. I was launching at the same time 5 different Orca calculation. Now, because --cpus-per-task could be None, 1, 2 or 3; the five calculation might enter in the same node or in another node with this amount of free CPU, but when Orca ask for the parallel jobs, fail again because there are not this amount of CPU on the node.
The solution that I found is pretty simple. On the .sh script for Slurm I putted this:
#SBATCH --mincpus=n*m
#SBATCH --ntasks=n
#SBATCH --cpus-per-task m
Instead of only:
#SBATCH --cpus-per-task m
Where n will be equal to the number of parallel jobs specified on the Orca input (%pal nprocs n end) and m the number of CPU that you want to use for each parallel Orca job.
In my case I used n = 12, m = 1. With the flag --mincpus I ensured to take a node with at least 12 CPU and allocated them. With the --cpus-per-task is pretty evident what this flag do (even for me :-) ), which, by the way, has a default value of 1 and I don't know if more than 1 CPU for each OpenMPI Orca job improve the velocity of the calculation. And --ntasks gives the information to Slurm of how many task you will do.
Of course if you know the number of task and the CPU per task is easy to know how many CPU you need to reserve, but I don't know if this is easy to Slurm too :-). So, to be sure that I allocate the correct number of CPU i used --mincpus flag, but maybe is not needed. The thing is that it works now ^_^.
It is also important to take into account the amount of memory that you declare in the input of Orca in order of do not exceed the available memory. For example, if you have 12 task and a RAM of 15000 MBi, the right amount of memory to declared should be no more than 15000/12 = 1250 MBi
I had a similar problem with parallel jobs before. The slurm also output not enough slots error.
My solution is to change parallel threads into parallel processes. For my system is to change
#SBATCH -c 24
into
#SBATCH -n 24
and everything works just fine.
Hello everyone I'm actually using a soft called RepeatMasker, in this pipeline I can run parallelized job via slurm with the command -pa
here is a doc about this command :
RepeatMasker -h
-pa(rallel) [number]
The number of sequence batch jobs [50kb minimum] to run in parallel.
RepeatMasker will fork off this number of parallel jobs, each
running the search engine specified. For each search engine
invocation ( where applicable ) a fixed the number of cores/threads
is used:
RMBlast 4 cores
To estimate the number of cores a RepeatMasker run will use simply
multiply the -pa value by the number of cores the particular search
engine will use.
so in a slurm batch script I should add :
#SBATCH --cpus-per-task=8
RepeatMakser -pa 2, right?
since 8/4 =2
But I wondered if I should also add others #SBATCH parameters or if --cpus-per-task is sufficient ?
Thanks al ot
I am using a fluids solver called IAMR and I am trying to make it execute faster via my schools cluster. I have options to add nodes and specify tasks, but I have no clue what the distinction is our what my simulation needs to run. I am trying to render a single simulation and so far the following slurm script has worked:
=============================
#!/bin/bash
#SBATCH --job-name=first_slurm_job
#SBATCH -N 10
#SBATCH -p debug_queue
#SBATCH --time=4:00:00 # format days-hh:mm:ss
./amr3d.gnu.MPI.OMP.ex inputs.3d.rt
==============================
Aside from not knowing how many nodes and tasks to request, I am not sure I am submitting the job correctly. In the IAMR guide it states:
For an MPI build, you can run in parallel using, e.g.:
mpiexec -n 4 ./amr2d.gnu.DEBUG.MPI.ex inputs.2d.bubble
But I am not using that line when I make the job submission. I asked a friend and they said: typically "tasks" means "MPI processes", so if you break your problem into 4 grids then the way AMReX works, you can have each MPI rank update one grid , so with 4 grids you would ask for 4 MPI processes. So does that mean I have to figure out how to make the grid split into 4 parts if I request 4 tasks? Any insight would help! Here are my clusters specs:
Cluster Specs
Yor file name us amr3d.gnu.MPI.OMP.ex. Is this a OpenMP program (parallel using multiple cores) or a MPI program (using multiple processes possible on multiple nodes) or a hybrid program using both like the filename sounds like?
Ok, it is a hybrid program, so we say you use 2 nodes with 16 cores each, then you can do it like
#!/bin/bash
#SBATCH --job-name=first_slurm_job
#SBATCH -p debug_queue
#SBATCH --time=4:00:00 # format days-hh:mm:ss
#SBATCH --cpus-per-task=16
#SBATCH --ntasks=2
export OMP_NUM_THREADS=16
echo "Used nodes:" $SLURM_NODELIST
mpirun ./amr3d.gnu.MPI.OMP.ex inputs.3d.rt
I am running mpi code on host1(quad core) and host2(dual core)
mpiexec -hosts host1,host2 -n 6 ./mytask
I want to assign 4 processes for host1 and 2 for host2. I tried --map-by core but I found that the processes are distributed 3 for each.
This is the mpiexec help output
mpiexec -h
Usage: ./mpiexec [global opts] [local opts for exec1] [exec1] [exec1 args] : [local opts for exec2] [exec2] [exec2 args] : ...
Global options (passed to all executables):
Global environment options:
-genv {name} {value} environment variable name and value
-genvlist {env1,env2,...} environment variable list to pass
-genvnone do not pass any environment variables
-genvall pass all environment variables not managed
by the launcher (default)
Other global options:
-f {name} file containing the host names
-hosts {host list} comma separated host list
-wdir {dirname} working directory to use
-configfile {name} config file containing MPMD launch options
Local options (passed to individual executables):
Local environment options:
-env {name} {value} environment variable name and value
-envlist {env1,env2,...} environment variable list to pass
-envnone do not pass any environment variables
-envall pass all environment variables (default)
Other local options:
-n/-np {value} number of processes
{exec_name} {args} executable name and arguments
Hydra specific options (treated as global):
Launch options:
-launcher launcher to use (ssh rsh fork slurm ll lsf sge manual persist)
-launcher-exec executable to use to launch processes
-enable-x/-disable-x enable or disable X forwarding
Resource management kernel options:
-rmk resource management kernel to use (user slurm ll lsf sge pbs cobalt)
Processor topology options:
-topolib processor topology library (hwloc)
-bind-to process binding
-map-by process mapping
-membind memory binding policy
Checkpoint/Restart options:
-ckpoint-interval checkpoint interval
-ckpoint-prefix checkpoint file prefix
-ckpoint-num checkpoint number to restart
-ckpointlib checkpointing library (none)
Demux engine options:
-demux demux engine (poll select)
Other Hydra options:
-verbose verbose mode
-info build information
-print-all-exitcodes print exit codes of all processes
-iface network interface to use
-ppn processes per node
-profile turn on internal profiling
-prepend-rank prepend rank to output
-prepend-pattern prepend pattern to output
-outfile-pattern direct stdout to file
-errfile-pattern direct stderr to file
-nameserver name server information (host:port format)
-disable-auto-cleanup don't cleanup processes on error
-disable-hostname-propagation let MPICH auto-detect the hostname
-order-nodes order nodes as ascending/descending cores
-localhost local hostname for the launching node
-usize universe size (SYSTEM, INFINITE, <value>)
Please see the intructions provided at
http://wiki.mpich.org/mpich/index.php/Using_the_Hydra_Process_Manager
for further details
There are several options.
pi#RPi:~ $ mpiexec -H rpi,rpi,rpi,rpi5,rpi7,rpi7 -np 6 helloworld.py
Hello World! I am process 3 of 6 on RPi5.
Hello World! I am process 5 of 6 on RPi7.
Hello World! I am process 4 of 6 on RPi7.
Hello World! I am process 0 of 6 on RPi.
Hello World! I am process 2 of 6 on RPi.
Hello World! I am process 1 of 6 on RPi.
The hostfile with the -hostfile filename.
pi#RPi:~ $ cat filename
RPi slots=4 max_slots=4
RPi5 slots=2 max_slots=2
RPi7 slots=4 max_slots=4
Also, use the -nooversubscribe option.
I am submitting a toy array job in slurm. My command line is
$ sbatch -p development -t 0:30:0 -n 1 -a 1-2 j1
where j1 is script:
#!/bin/bash
echo job id is $SLURM_JOB_ID
echo array job id is $SLURM_ARRAY_JOB_ID
echo task id id $SLURM_ARRAY_TASK_ID
When I submit this, I get an error:
--> Verifying valid submit host (login1)...OK
--> Verifying valid jobname...OK
--> Enforcing max jobs per user...OK
--> Verifying availability of your home dir (/home1/03400/myname)...OK
--> Verifying availability of your work dir (/work/03400/myname)...OK
--> Verifying availability of your scratch dir (/scratch/03400/myname)...OK
--> Verifying valid ssh keys...OK
--> Verifying access to desired queue (development)...OK
--> Verifying job request is within current queue limits...OK
--> Checking available allocation (PRJ-1234)...OK
sbatch: error: Batch job submission failed: Invalid job array specification
The same job works fine without the array specification:
$ sbatch -p development -t 0:30:0 -n 1 j1
This post is a bit old, but in case it happens for other people, I have had the same issue but the accepted answer did not suggest what was the problem in my case.
This error (sbatch: error: Batch job submission failed: Invalid job array specification) can also be raised when the array size is too large.
From https://slurm.schedmd.com/slurm.conf.html
MaxArraySize
The maximum job array size. The maximum job array task index value will be one less than MaxArraySize to allow for an index value of zero. Configure MaxArraySize to 0 in order to disable job array use. The value may not exceed 4000001. The value of MaxJobCount should be much larger than MaxArraySize. The default value is 1001.
To check the value, the slurm.conf file should be accessible by all slurm users (still according to 1) and may be found somewhere near /etc/slurm.conf (see https://slurm.schedmd.com/slurm.conf.html#lbAM, in my case I found it at path /etc/slurm/slurm.conf).
The syntax for your array specification is correct. But the printout you paste is not standard Slurm, I guess you are working on Stampede ; they have their own sbatch wrapper.
What you could do is use the -vvv option to sbatch to see exactly what Slurm sees:
$ sbatch -vvv -p development -t 0:30:0 -n 1 -a 1-2 j1 |& grep array
This should return
sbatch: array : 1-2
and if it does not it means the information is somehow lost somewhere.
What you can try is remove the array specification from the submission command line and insert it in the submission script, like this:
$ sbatch -p development -t 0:30:0 -n 1 j1
with j1 being
#!/bin/bash
#SBATCH -a 1-2
echo job id is $SLURM_JOB_ID
echo array job id is $SLURM_ARRAY_JOB_ID
echo task id id $SLURM_ARRAY_TASK_ID
The next step is to contact the system administrators with the information you will get from running the above tests and ask for help.