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# Capacity Computing
## Introduction
In many cases, it is useful to submit a huge (>100) number of computational jobs into the PBS queue system. A huge number of (small) jobs is one of the most effective ways to execute embarrassingly parallel calculations, achieving the best runtime, throughput, and computer utilization.
However, executing a huge number of jobs via the PBS queue may strain the system. This strain may result in slow response to commands, inefficient scheduling, and overall degradation of performance and user experience for all users. For this reason, the number of jobs is **limited to 100 jobs per user, 4,000 jobs and subjobs per user, 1,500 subjobs per job array**.
!!! note
Follow one of the procedures below, in case you wish to schedule more than 100 jobs at a time.
* Use [Job arrays][1] when running a huge number of [multithread][2] (bound to one node only) or multinode (multithread across several nodes) jobs.
* Use [HyperQueue][3] when running single core jobs.
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## Policy
1. A user is allowed to submit at most 100 jobs. Each job may be [a job array][1].
1. The array size is at most 1,000 subjobs.
## Job Arrays
!!! note
A huge number of jobs may easily be submitted and managed as a job array.
A job array is a compact representation of many jobs called subjobs. Subjobs share the same job script, and have the same values for all attributes and resources, with the following exceptions:
* each subjob has a unique index, $PBS_ARRAY_INDEX
* job Identifiers of subjobs only differ by their indices
* the state of subjobs can differ (R, Q, etc.)
All subjobs within a job array have the same scheduling priority and schedule as independent jobs. An entire job array is submitted through a single `qsub` command and may be managed by `qdel`, `qalter`, `qhold`, `qrls`, and `qsig` commands as a single job.
### Shared Jobscript
All subjobs in a job array use the very same single jobscript. Each subjob runs its own instance of the jobscript. The instances execute different work controlled by the `$PBS_ARRAY_INDEX` variable.
Example:
Assume we have 900 input files with the name of each beginning with "file" (e.g. file001, ..., file900). Assume we would like to use each of these input files with myprog.x program executable, each as a separate job.
First, we create a tasklist file (or subjobs list), listing all tasks (subjobs) - all input files in our example:
```console
$ find . -name 'file*' > tasklist
```
Then we create a jobscript:
#### Salomon
```bash
#!/bin/bash
#PBS -A PROJECT_ID
#PBS -q qprod
#PBS -l select=1:ncpus=24,walltime=02:00:00
# change to scratch directory
SCR=/scratch/work/user/$USER/$PBS_JOBID
mkdir -p $SCR ; cd $SCR || exit
# get individual tasks from tasklist with index from PBS JOB ARRAY
TASK=$(sed -n "${PBS_ARRAY_INDEX}p" $PBS_O_WORKDIR/tasklist)
# copy input file and executable to scratch
cp $PBS_O_WORKDIR/$TASK input ; cp $PBS_O_WORKDIR/myprog.x .
# execute the calculation
./myprog.x < input > output
# copy output file to submit directory
cp output $PBS_O_WORKDIR/$TASK.out
```
In this example, the submit directory contains the 900 input files, the myprog.x executable, and the jobscript file. As an input for each run, we take the filename of the input file from the created tasklist file. We copy the input file to the local scratch memory `/lscratch/$PBS_JOBID`, execute the myprog.x and copy the output file back to the submit directory, under the `$TASK.out` name. The myprog.x executable runs on one node only and must use threads to run in parallel. Be aware, that if the myprog.x **is not multithreaded**, then all the **jobs are run as single-thread programs in a sequential manner**. Due to the allocation of the whole node, the accounted time is equal to the usage of the whole node, while using only 1/16 of the node.
If running a huge number of parallel multicore (in means of multinode multithread, e.g. MPI enabled) jobs is needed, then a job array approach should be used. The main difference, as compared to the previous examples using one node, is that the local scratch memory should not be used (as it is not shared between nodes) and MPI or other techniques for parallel multinode processing has to be used properly.
### Submit the Job Array
To submit the job array, use the `qsub -J` command. The 900 jobs of the [example above][5] may be submitted like this:
#### Salomon
```console
$ qsub -N JOBNAME -J 1-900 jobscript
506493[].isrv5
```
In this example, we submit a job array of 900 subjobs. Each subjob will run on one full node and is assumed to take less than 2 hours (note the #PBS directives in the beginning of the jobscript file, do not forget to set your valid PROJECT_ID and desired queue).
Sometimes for testing purposes, you may need to submit a one-element only array. This is not allowed by PBSPro, but there is a workaround:
```console
$ qsub -N JOBNAME -J 9-10:2 jobscript
```
This will only choose the lower index (9 in this example) for submitting/running your job.
### Manage the Job Array
Check status of the job array using the `qstat` command.
```console
$ qstat -a 12345[].dm2
dm2:
Req'd Req'd Elap
Job ID Username Queue Jobname SessID NDS TSK Memory Time S Time
--------------- -------- -- |---|---| ------ --- --- ------ ----- - -----
12345[].dm2 user2 qprod xx 13516 1 16 -- 00:50 B 00:02
```
When the status is B, it means that some subjobs are already running.
Check the status of the first 100 subjobs using the `qstat` command.
```console
$ qstat -a 12345[1-100].dm2
dm2:
Req'd Req'd Elap
Job ID Username Queue Jobname SessID NDS TSK Memory Time S Time
--------------- -------- -- |---|---| ------ --- --- ------ ----- - -----
12345[1].dm2 user2 qprod xx 13516 1 16 -- 00:50 R 00:02
12345[2].dm2 user2 qprod xx 13516 1 16 -- 00:50 R 00:02
12345[3].dm2 user2 qprod xx 13516 1 16 -- 00:50 R 00:01
12345[4].dm2 user2 qprod xx 13516 1 16 -- 00:50 Q --
. . . . . . . . . . .
, . . . . . . . . . .
12345[100].dm2 user2 qprod xx 13516 1 16 -- 00:50 Q --
```
Delete the entire job array. Running subjobs will be killed, queueing subjobs will be deleted.
```console
$ qdel 12345[].dm2
```
Deleting large job arrays may take a while.
Display status information for all user's jobs, job arrays, and subjobs.
```console
$ qstat -u $USER -t
```
Display status information for all user's subjobs.
```console
$ qstat -u $USER -tJ
```
For more information on job arrays, see the [PBSPro Users guide][6].
HyperQueue lets you build a computation plan consisting of a large amount of tasks and then execute it transparently over a system like SLURM/PBS. It dynamically groups jobs into SLURM/PBS jobs and distributes them to fully utilize allocated nodes. You thus do not have to manually aggregate your tasks into SLURM/PBS jobs. See the [project repository][a].
### Features
* **Transparent task execution on top of a Slurm/PBS cluster***
Automatic task distribution amongst jobs, nodes, and cores.
* **Dynamic load balancing across jobs**
Work-stealing scheduler<br>NUMA-aware, core planning, task priorities, task arrays<br> Nodes and tasks may be added/removed on the fly
* **Scalable**
Low overhead per task (~100μs)<br>Handles hundreds of nodes and millions of tasks<br>Output streaming avoids creating many files on network filesystems
* **Easy deployment**
Single binary, no installation, depends only on *libc*<br>No elevated privileges required
* **Open source**
To install/compile HyperQueue, follow the steps on the [official webpage][b].
* Start server (e.g. on a login node or in a cluster partition)
* Submit a job (command `echo 'Hello world'` in this case)
`$ qsub <your-params-of-qsub> -- hq worker start`
``$ qsub <your-params-of-qsub> -- `which pbsdsh` hq worker start``
## Examples
Download the examples in [capacity.zip][9], illustrating the above listed ways to run a huge number of jobs. We recommend trying out the examples before using this for running production jobs.
Unzip the archive in an empty directory on cluster and follow the instructions in the README file-
```console
$ unzip capacity.zip
$ cat README
```
[1]: #job-arrays
[2]: #shared-jobscript-on-one-node
[5]: ##shared-jobscript
[6]: ../pbspro.md
[9]: capacity.zip
[a]: https://github.com/It4innovations/hyperqueue
[b]: https://it4innovations.github.io/hyperqueue/install/