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    Creating a batch job script for Mahti

    Please have a look at the Puhti documentation for the general introduction to batch scripts in the CSC supercomputing environment. On this page we focus on Mahti specific topics.

    Note

    Mahti does not have GPUs, NVMe disk on compute nodes, or the need to reserve memory. Instead, full nodes are allocated for jobs, with the exception of interactive jobs, also see below. Many options also work differently in Puhti and Mahti, so it is not advisable to copy scripts from Puhti to Mahti.

    Basic MPI batch jobs

    An example of a simple MPI batch job script:

    #!/bin/bash
#SBATCH --job-name=myTest
#SBATCH --account=<project>
#SBATCH --time=02:00:00
#SBATCH --partition=medium
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=128

module load myprog/1.2.3

srun myprog -i input -o output

    Specify the exact number of nodes and number of tasks per node with --nodes and --ntasks-per-node, respectively. Use all 128 cores in the node.

    Note

    • MPI processes should not be started with mpirun or mpiexec. Use srun instead.
    • appropriate software module has to be loaded in the batch job script for the submission to work properly.

    Hybrid batch jobs

    As explained for Puhti, hybrid parallelization can run multiple OpenMP threads per MPI task. In addition to the --ntasks-per-node=X one needs to set --cpus-per-task=Y. The default is one cpu (thread) per task. To use all physical cores in a Mahti node choose X * Y = 128, like in this example. If you are using simultaneous multithreading (see section below) your should use X * Y = 256

    The optimal ratio between the number of tasks and cores per tasks varies for each program and job input. Testing is required to find the right combination for your application. You can find some examples for cp2k and NAMD.

    Hybrid batch jobs with simultaneous multithreading (SMT)

    Mahti is configured so that it doesn't place any theads to the logical cores by default. SMT support can be enabled with --hint=multithread option. When this option is used, it is important to use the --ntasks-per-node=X and --cpus-per-task=Y so that X * Y = 256. Failing to do so will leave some of the actual physical cores unallocated and performance will be suboptimal. Example batch job script can be found here.

    Undersubscribing nodes

    If an application requires more memory per core than there is available with full node (2 GB / core) it is possible to use also a subset of cores within a node. Also, if the application is memory bound, memory bandwidth and the application performance can be improved by using only a single core per NUMA domain or L3 cache (look here for details about Mahti architecture). Note that billing is, however, always based on full nodes.

    When undersubscribing nodes, one should always set --ntasks-per-node=X and --cpus-per-task=Y so that X * Y = 128, even with pure MPI jobs. By default, Slurm scatters MPI tasks --cpus-per-task apart, i.e. with --cpus-per-task=8 the MPI task 0 is bound to CPU core 0, the MPI task 1 is bound to CPU core 7 etc.. Memory bandwidth (and application performance) is the best when the tasks are executing on maximally scattered cores. As an example, in order to use 32 GB / core, one can run only with 8 tasks per node as

    #SBATCH --ntasks-per-node=8
#SBATCH --cpus-per-task=16

module load myprog/1.2.3
export OMP_NUM_THREADS=1

srun myprog -i input -o output

    For hybrid applications, one should use OMP_PROC_BIND OpenMP runtime environment variable for placing the OpenMP threads. As an example, in order to run one MPI task per NUMA domain and one OpenMP thread per L3cache one can set

    #SBATCH --ntasks-per-node=8
#SBATCH --cpus-per-task=16

export OMP_NUM_THREADS=4
export OMP_PROC_BIND=spread

module load myprog/1.2.3

srun myprog -i input -o output

    Please check also our Mahti batch script examples page.

    Using interactive partition for non-parallel pre- or post-processing

    In many cases the computing tasks include pre- or post-processing steps that are not able to utilize parallel computing. In these cases it is recommended that, if possible, the task is split into several, chained, batch jobs and that the non-parallel processing is executed in the interactive partition of Mahti.

    In the interactive partition the jobs can reserve just few cores so that the non-parallel tasks can be executed without wasting resources.
    Note that you can use interactive partition also for non-interactive jobs and that you can link two batch jobs so that the second job starts only when the first one has finished.

    For example, say that we would like to post-process the output file, produced with the very first MPI example job in this page. The post processing command: python post-proc.py output uses only serial computing and requires about 40 minutes and 3 GB of memory. Instead of including the post-processing to the main job it is reasonable to execute it as separate job in the interactive partition.

    Jobs in interactive partition can reserve 1-8 cores and each core reserves 1,875 GB of memory. Thus in this case we will reserve 2 cores --cpus-per-task=2 to have enough memory (3,75 GB) available. Further, --dependency=afterok:<slurm-jobid> defines that the job can start only when the previously sent job has successfully finished. Here the <slurm-jobid> is replaced with ID number of the batch job that produces the output file (you'll get the ID number when you submit the job).

    #!/bin/bash
#SBATCH --job-name=post-process-myTest
#SBATCH --account=<project>
#SBATCH --time=00:50:00
#SBATCH --partition=interactive
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=2
#SBATCH --dependency=afterok:<slurm-jobid>

python post-proc.py output

    Executing large amounts of small non-MPI jobs

    In Mahti, GREASY meta sheduler can be used to process large amounts of small non-MPI jobs. Please check the GREASY instructon page for more details. * GREASY in Mahti

    Last edited Tue Dec 15 2020