Skip to content

Best Practices for Jobs

Do Not Run Production Jobs in Global Homes

As a general best practice, users should do production runs from $SCRATCH instead of $HOME.

$HOME is meant for permanent and relatively small storage. It is not tuned to perform well for parallel jobs. Home is perfect for storing files such as source codes and shell scripts, etc. Please note that while building software in /global/home is generally good, it is best to install dynamic libraries that are used on compute nodes in global common for best performance.

$SCRATCH is meant for large and temporary storage. It is optimized for read and write operations. $SCRATCH is perfect for staging data and performing parallel computations. Running in $SCRATCH also helps to improve the responsiveness of the global file systems (global homes and global project) in general.

Specify account

For users who are members of multiple NERSC repositories charges are made to the default account, as set in Iris, unless the #SBATCH --account=<NERSC repository> flag has been set. It is good practice to always set the account flag to ensure the appropriate allocation is charged.

Time Limits

Due to backfill scheduling, short and variable-length jobs generally start quickly resulting in much better job throughput.

#SBATCH --time-min=<lower_bound>
#SBATCH --time=<upper_bound>

Long Running Jobs

Simulations which must run for a long period of time achieve the best throughput when composed of many small jobs utilizing checkpoint/restart chained together.

Limit Your Queries to the Batch System

We recommend you limit the rate at which your jobs query the batch system to an aggregate of 1 - 2 times / minutes. This includes all Slurm queries such as squeue, sbatch, srun, or other Slurm commands. Keep in mind this is an aggregate rate across all your jobs, so if you have a single job that queries once a minute but 500 of these jobs start at once Slurm will see a rate of 500 queries / minute, so please scale your rate accordingly.

If too many users query the scheduler at once it can become overwhelmed and become unable to schedule user jobs. To avoid this, Slurm has implemented an algorithm to limit users who make a very high rate of Slurm queries. Once a user exceeds this rate, the scheduler will pause briefly before responding to the query. This means that if you have a high rate of Slurm queries, you will be spending your compute time waiting for a response from the scheduler which is not an efficient use of compute time.

Improve Efficiency by Preparing User Environment Before Running

In general, compute nodes are optimized for processing data and running simulations. Users should use login nodes for compilations, environment setup and preprocessing small inputs, in order to utilize compute resources efficiently.

Using the Linux here document as in the example below will run those commands to prepare the user environment for the batch job on the login node to help improve job efficiency and save computing cost of the batch job. It can also help to alleviate the burden on the global home file system. This script also keeps the user environment needed for the batch job in a single file.


This is an example of a script to prepare the user environment on a login node, propagate this environment to a batch job, and submit the batch job. This can be accomplished in a single script.

You could do so by preparing a file named in the example below, and running it as ./ on a login node. This script:

  • Sets up the user environment for the batch job first on a login node, such as loading modules, setting environment variables, or copying input files, etc.;
  • Creates a batch script named;
  • Submits this job will inherit the user environment just set earlier in the script.
#!/bin/bash -l

# Submit this script as: "./" instead of "sbatch"

# Prepare user env needed for Slurm batch job
# such as module load, setup runtime environment variables, or copy input files, etc.
# Basically, these are the commands you usually run ahead of the srun command 

module load cray-hdf5

# Generate the Slurm batch script below with the here document, 
# then when sbatch the script later, the user env set up above will run on the login node
# instead of on a head compute node (if included in the Slurm batch script),
# and inherited into the batch job.

cat << EOF > 
#SBATCH -t 30:00
#SBATCH -q debug
#SBATCH -C cpu

srun -n 16 -c 32 --cpu_bind=cores ./myapp.exe 

# Other commands needed after srun, such as copy your output filies,
# should still be included in the Slurm script.
cp <my_output_file> <target_location>/.

# Now submit the batch job

I/O Performance

Perlmutter has a dedicated large, local, parallel scratch file system. The scratch file system is intended for temporary uses such as storage of checkpoints or application input and output. Data and I/O intensive applications should use the local scratch file system.

Theis should be referenced with the environment variable $SCRATCH.


The Scratch File System is not backed up and old files are subject to purging.

File System Licenses

Users should specify the file systems their jobs will use with the sbatch license flag, -L or --licenses. A batch job will not start if any of the specified file systems are unavailable due to maintenance or an outage. The following example specifies that a job uses both the scratch and community file systems.

#SBATCH -L scratch,cfs


#SBATCH --licenses=scratch,cfs

Available Licenses

  • scratch (or SCRATCH)
  • cfs
  • dna
  • cvmfs
  • hpss (Perlmutter only)

Licenses can also be added or adjusted after submission with scontrol update job=<jobid> Licenses=<comma separated list of licenses>

Use sbcast for Large Jobs

Large jobs (>1000 MPI tasks) may take longer to start up. One solution may be to copy the executable to a local path on the compute nodes allocated to the job, instead of loading it onto the compute nodes from a slow file system such as the home.

Users can copy the executable to the compute nodes before the actual computation using the Slurm sbcast command or the srun --bcast flag. Making the executable available local to the compute node, e.g. in /tmp could speed up the job startup time compared to running executables from a network file system.

For example, assuming exe_on_slow_fs is the executable filename, which resides on a slow file system such as the user home, you can modify the line of srun in your submit script from this:

srun exe_on_slow_fs

to this:

srun --bcast=/tmp/${USER}_exe_filename exe_on_slow_fs

or to this:

sbcast exe_on_slow_fs /tmp/${USER}_exe_filename
srun /tmp/${USER}_exe_filename

Make sure to choose a temporary file name unique to your computation (e.g. include your username with the variable $USER), or you may receive permission denied errors if trying to overwrite someone else's files.


There is no real downside to broadcasting the executable with Slurm, so it can be used with jobs at any scale, especially if you experience timeout errors associated with MPI_Init().


Besides the executable, you can also sbcast other large files, such as input files, shared libraries, etc. It would be easier to create a tar file to sbcast, then untar on the compute nodes before the actual srun instead of sbcasting multiple individual files.


You could also set the destination to be a file path (such as /tmp/${$USER}_dir/) instead of a file name. Make sure the file path already exists (or to create one) and to include the "/" at the end of the path name in the sbcast and srun commands.

Core Specialization

Core specialization is a feature designed to isolate system overhead (system interrupts, etc.) to designated cores on a compute node. Setting aside 1 or 2 cores for core specialization is recommended.

The srun flag for core specialization is -S or --core-spec. It only works in a batch script with sbatch. It can not be requested as a flag with salloc for interactive jobs, since salloc is already a wrapper script for srun.

Process Placement

Several mechanisms exist to control process placement on NERSC's Cray systems. Application performance can depend strongly on placement depending on the communication pattern and other computational characteristics.

Examples below are run on Perlmutter.


elvis@perlmutter$ salloc -N 4 -C cpu -q interactive -t 20:00
elvis@nid004175$ srun -n 8 -c 2 | sort -nk 4
Hello from rank 0, on nid004175. (core affinity = 0,128)
Hello from rank 1, on nid004175. (core affinity = 16,144)
Hello from rank 2, on nid004622. (core affinity = 0,128)
Hello from rank 3, on nid004622. (core affinity = 16,144)
Hello from rank 4, on nid006290. (core affinity = 0,128)
Hello from rank 5, on nid006290. (core affinity = 16,144)
Hello from rank 6, on nid006430. (core affinity = 0,128)
Hello from rank 7, on nid006430. (core affinity = 16,144)


The MPICH_RANK_REORDER_METHOD environment variable is used to specify other types of MPI task placement. For example, setting it to 0 results in a round-robin placement:

elvis@nid004175$ MPICH_RANK_REORDER_METHOD=0 srun -n 8 -c 2 | sort -nk 4
Hello from rank 0, on nid004175. (core affinity = 1,129)
Hello from rank 1, on nid004622. (core affinity = 1,129)
Hello from rank 2, on nid006290. (core affinity = 1,129)
Hello from rank 3, on nid006430. (core affinity = 1,129)
Hello from rank 4, on nid004175. (core affinity = 17,145)
Hello from rank 5, on nid004622. (core affinity = 17,145)
Hello from rank 6, on nid006290. (core affinity = 17,145)
Hello from rank 7, on nid006430. (core affinity = 17,145)

There are other modes available with the MPICH_RANK_REORDER_METHOD environment variable, including one which lets the user provide a file called MPICH_RANK_ORDER which contains a list of each task's placement on each node. These options are described in detail in the intro_mpi man page.


For MPI applications which perform a large amount of nearest-neighbor communication, e.g., stencil-based applications on structured grids, Cray provides a tool in the perftools-base module called grid_order which can generate a MPICH_RANK_ORDER file automatically by taking as parameters the dimensions of the grid, core count, etc. For example, to place MPI tasks in row-major order on a Cartesian grid of size (4, 4, 4), using 32 tasks per node on Perlmutter:

perlmutter$ module load perftools-base
perlmutter$ grid_order -R -c 32 -g 4,4,4
# grid_order -R -Z -c 32 -g 4,4,4
# Region 3: 0,0,1 (0..63)

One can then save this output to a file called MPICH_RANK_ORDER and then set MPICH_RANK_REORDER_METHOD=3 before running the job, which tells Cray MPI to read the MPICH_RANK_ORDER file to set the MPI task placement. For more information, please see the man page man grid_order (available when the perftools-base module is loaded) on Perlmutter.


Huge pages are virtual memory pages which are bigger than the default page size of 4K bytes. Huge pages can improve memory performance for common access patterns on large data sets since it helps to reduce the number of virtual to physical address translations than compared with using the default 4K. Huge pages also increase the maximum size of data and text in a program accessible by the high speed network, and reduce the cost of accessing memory, such as in the case of many MPI_Alltoall operations. Using hugepages can help to reduce the application runtime variability.

To use hugepages for an application (with the 2M hugepages as an example):

module load craype-hugepages2M
cc -o mycode.exe mycode.c

And also load the same hugepages module at runtime.

Due to the hugepages memory fragmentation issue, applications may get "Cannot allocate memory" warnings or errors when there are not enough hugepages on the compute node, such as:

libhugetlbfs [nid000xx:xxxxx]: WARNING: New heap segment map at 0x10000000 failed: Cannot allocate memory

When to Use Huge Pages

  • For MPI applications, map the static data and/or heap onto huge pages.
  • For an application which uses shared memory, which needs to be concurrently registered with the high speed network drivers for remote communication.
  • For SHMEM applications, map the static data and/or private heap onto huge pages.
  • For applications written in Unified Parallel C, Coarray Fortran, and other languages based on the PGAS programming model, map the static data and/or private heap onto huge pages.
  • For an application doing heavy I/O.
  • To improve memory performance for common access patterns on large data sets.

When to Avoid Huge Pages

  • Applications sometimes consist of many steering programs in addition to the core application. Applying huge page behavior to all processes would not provide any benefit and would consume huge pages that would otherwise benefit the core application. The runtime environment variable HUGETLB_RESTRICT_EXE can be used to specify the subset of the programs to use hugepages.

  • For certain applications if using hugepages either causes issues or slowing down performances, users can explicitly unload the craype-hugepages2M module. One such example is that when an application forks more subprocesses (such as pthreads) and allocate memory, the newly allocated memory are the small 4K pages.

Task Packing

Users requiring large numbers of single-task jobs have several options at NERSC. The options include:

  • Submitting jobs to the shared QOS,
  • Using a workflow tool to combine the tasks into one larger job,
  • Using job arrays to submit many individual jobs which look very similar.

If you have a large number of independent serial jobs (that is, the jobs do not have dependencies on each other), you may wish to pack the individual tasks into one bundled Slurm job to help with queue throughput. Packing multiple tasks into one Slurm job can be done via multiple srun commands in the same job script (example).