Advanced MPI scheduling

Most users should submit MPI or distributed memory parallel jobs following the example given at Running jobs. Simply request a number of processes with --ntasks or -n and trust the scheduler to allocate those processes in a way that balances the efficiency of your job with the overall efficiency of the cluster.

If you want more control over how your job is allocated, then SchedMD's page on multicore support is a good place to begin. It describes how many of the options to the sbatch command interact to constrain the placement of processes.

You may find this discussion of What exactly is considered a CPU? in SLURM to be useful.

Examples of common MPI scenarios

Few cores, any nodes

In addition to the time limit needed for any Slurm job, an MPI job requires that you specify how many MPI processes Slurm should start. The simplest way to do this is with --ntasks. Since the default memory allocation of 256MB per core is often insufficient, you may also wish to specify how much memory is needed. Using --ntasks you cannot know in advance how many cores will reside on each node, so you should request memory with --mem-per-cpu. For example:

 --ntasks=15
--mem-per-cpu=3G
srun application.exe

This will run 15 MPI processes. The cores could be allocated on one node, on 15 nodes, or on any number in between.

Whole nodes

Most nodes in Cedar and Graham have 32 cores and 128GB or more of memory. If you have a large parallel job to run, which can use 32 or a multiple of 32 cores efficiently, you should request whole nodes like so:

--nodes=2
--ntasks-per-node=32
--mem=128000M
srun application.exe

The above job will probably start sooner than an equivalent one that requests --ntasks=64.

You should use --mem=128000M rather than --mem=128G when requesting whole nodes because a small tranche of memory is reserved to the operating system, and requesting precisely 128GB means the job cannot be scheduled on the plentiful 128GB nodes. The job will not be rejected by Slurm, it will just wait much longer to start than it needs to.

Few cores, single node

If for some reason you need less than 32 cores but they must all in a single node, then you can request,

--nodes=1
--ntasks-per-node=15
--mem=45G
srun application.exe

In this case you could also say --mem-per-cpu=3G. The advantage of --mem=45G is that the memory consumed by each individual process doesn't matter, as long as all of them together don’t use more than 45GB. With --mem-per-cpu=3G, the job will be canceled if any of the processes exceeds 3GB.

Large parallel job, not a multiple of 32 cores

Not every application runs with maximum efficiency on a multiple of 32 cores. Choosing the number of cores to request, and whether or not to request whole nodes, may be a trade-off between running time (or efficient use of the computer) and waiting time (or efficient use of your time). If you want help evaluating these factors, please write to support@computecanada.ca.

Hybrid jobs: MPI and OpenMP, or MPI and threads

It is important to understand that the number of tasks requested of Slurm is the number of processes that will be started by srun. So for a hybrid job that will use both MPI processes and OpenMP threads or Posix threads, you should set the MPI process count with --ntasks or -ntasks-per-node, and set the thread count with --cpus-per-task.

--ntasks=16
--cpus-per-task=4
--mem-per-cpu=3G
srun application.exe

In this example a total of 64 cores will be allocated, but only 16 MPI processes (tasks) can and will be initialized. If the application is also OpenMP, then each process will spawn 4 threads, one per core. Each process will be allocated with 12GB of memory. The tasks, with 4 cores each, could be allocated anywhere, from 2 to up to 16 nodes.

--nodes=2
--ntasks-per-node=8
--cpus-per-task=4
--mem=96G
srun application.exe

This job is the same size as the last one: 16 tasks (that is, 16 MPI processes), each with 4 threads. The difference here is that we are sure of getting exactly 2 whole nodes. Recall that --mem requests memory per node, so we use it instead of --mem-per-cpu for the reason described earlier.

Why srun instead of mpiexec or mpirun?

mpirun is a wrapper that enables communication between processes running on different machines. Modern schedulers already provide many things that mpirun needs. With Torque/Moab, for example, there is no need to pass to mpirun the list of nodes on which to run, or the number of processes to launch; this is done automatically by the scheduler. With Slurm, the task affinity is also resolved by the scheduler, so there is no need to specify things like

mpirun --map-by node:pe=4 -n 16  application.exe

As implied in the examples above, srun application.exe will automatically distribute the processes to precisely the resources allocated to the job.

In programming terminology, srun is higher level of abstraction than mpirun. Anything that can be done with mpirun can be done with srun, and more. It is the tool in Slurm to distribute any kind of computations. It replaces Torque’s pbsdsh, for example, and much more. Think of srun as the SLURM "all-around parallel-tasks distributor"; once a particular set of resources is allocated, the nature of your application doesn't matter (MPI, OpenMP, hybrid, serial farming, pipelining, multi-program, etc.), you just have to srun it

Also, as you would expect, srun is fully coupled to Slurm. When you srun an application, a "job step" is started, the environment variables SLURM_STEP_ID and SLURM_PROCID are initialized correctly, and correct accounting information is recorded.

External links

• sbatch documentation
• srun documentation
• Open MPI and SLURM

Message Passing Interface