Skip to content

perlmutter.rst

Latest commit

 

History

History
198 lines (127 loc) · 6.82 KB

perlmutter.rst

File metadata and controls

198 lines (127 loc) · 6.82 KB
Raw

Perlmutter

Perlmutter is an HPE Cray "Shasta" system located at NERSC. Its compute nodes are equipped with four A100 NVIDIA GPUs.

It uses the SLURM scheduler to submit jobs from login nodes to run on the compute nodes.

Configuring Python and Installation

Begin by loading the python module. The following modules are recommended:

module load python

Create a conda environment

You can create a conda environment in which to install libEnsemble and all dependencies. For example:

conda create -n libe-pm python=3.10 -y

As Perlmutter has a shared HOME filesystem with other clusters, using the -pm suffix (for Perlmutter) is good practice.

Activate your virtual environment with:

export PYTHONNOUSERSITE=1
conda activate libe-pm

Installing libEnsemble and dependencies

Having loaded the Anaconda Python module, libEnsemble can be installed by one of the following ways.

  1. Install via pip into the environment.
pip install libensemble
  1. Install via conda:
conda config --add channels conda-forge
conda install -c conda-forge libensemble

See :doc:`advanced installation<../advanced_installation/advanced_installation>` for other installation options.

Job Submission

Perlmutter uses Slurm for job submission and management. The two most common commands for initiating jobs are salloc and sbatch for running in interactive and batch modes, respectively. libEnsemble runs on the compute nodes on Perlmutter using either multi-processing (recommended) or mpi4py.

While libEnsemble should detect Perlmutter settings, you can ensure this by setting one of the following environment variables in the submission script or interactive session for either the CPU or GPU partitions of Perlmutter:

export LIBE_PLATFORM="perlmutter_c"  # For CPU partition
export LIBE_PLATFORM="perlmutter_g"  # For GPU partition

Example

To run the :doc:`forces_gpu<../tutorials/forces_gpu_tutorial>` tutorial on Perlmutter.

To obtain the example you can git clone libEnsemble - although only the forces sub-directory is needed:

git clone https://github.com/Libensemble/libensemble
cd libensemble/libensemble/tests/scaling_tests/forces/forces_app

To compile forces:

module load PrgEnv-nvidia cudatoolkit craype-accel-nvidia80
cc -DGPU -O3 -fopenmp -mp=gpu -target-accel=nvidia80 -o forces.x forces.c

Now go to forces_gpu directory:

cd ../forces_gpu

Now grab an interactive session on one node:

salloc -N 1 -t 20 -C gpu -q interactive -A <project_id>

Then in the session run:

export LIBE_PLATFORM="perlmutter_g"
python run_libe_forces.py -n 4

The workers will each run simualations with one GPU each.

To see GPU usage, ssh into the node you are on in another window and run:

watch -n 0.1 nvidia-smi

To watch video

There is a video demonstration of the forces example on Perlmutter.

Note

The video uses libEnsemble version 0.9.3, where some adjustments of the scripts are needed to run on Perlmutter. These adjustments are no longer necessary. libEnsemble now correctly detects MPI runner and GPU setting on Perlmutter and the GPU code runs with many more particles than the CPU version (forces_simple).

Example submission scripts are also given in the :doc:`examples<example_scripts>`.

Running libEnsemble with mpi4py

Running libEnsemble with local comms is usually sufficient on Perlmutter. However, if you need to use mpi4py, you should install and run as follows:

module load PrgEnv-gnu cudatoolkit
MPICC="cc -target-accel=nvidia80 -shared" pip install --force --no-cache-dir --no-binary=mpi4py mpi4py

This line will build mpi4py on top of a CUDA-aware Cray MPICH.

To run using 5 workers (one manager):

export SLURM_EXACT=1
srun -n 6 python my_script.py

More information on using Python and mpi4py on Perlmutter can be found in the Python on Perlmutter documentation.

Perlmutter FAQ

Some FAQs specific to Perlmutter. See more on the :doc:`FAQ<../FAQ>` page.

.. dropdown:: **srun: Job \*\*\*\*\*\* step creation temporarily disabled, retrying (Requested nodes are busy)**

   Having created a dir ``/ccs/proj/<project_id>/libensemble``:

   You may also see: ``srun: Job ****** step creation still disabled, retrying (Requested nodes are busy)``

   This error has been encountered on Perlmutter. It is recommended to add these lines to submission scripts::

       export SLURM_EXACT=1
       export SLURM_MEM_PER_NODE=0

   and to **avoid** using ``#SBATCH`` commands that may limit resources to srun job steps such as::

       #SBATCH --ntasks-per-node=4
       #SBATCH --gpus-per-task=1

   Instead provide these to sub-tasks via the ``extra_args`` option to
   the :doc:`MPIExecutor<../executor/ex_index>` ``submit`` function.


.. dropdown:: **GTL_DEBUG: [0] cudaHostRegister: no CUDA-capable device is detected**

   If using the environment variable ``MPICH_GPU_SUPPORT_ENABLED``, then ``srun`` commands, at
   time of writing, expect an option for allocating GPUs (e.g.~ ``--gpus-per-task=1`` would
   allocate one GPU to each MPI task of the MPI run). It is recommended that tasks submitted
   via the :doc:`MPIExecutor<../executor/ex_index>` specify this in the ``extra_args``
   option to the ``submit`` function (rather than using an ``#SBATCH`` command). This is needed
   even when using setting ``CUDA_VISIBLE_DEVICES`` or other options.

   If running the libEnsemble user calling script with ``srun``, then it is recommended that
   ``MPICH_GPU_SUPPORT_ENABLED`` is set in the user ``sim_f`` or ``gen_f`` function where
   GPU runs will be submitted, instead of in the batch script. E.g::

       os.environ["MPICH_GPU_SUPPORT_ENABLED"] = "1"


.. dropdown:: **warning: /tmp/pgcudafatYDO6wtSva6K2.o: missing .note.GNU-stack section implies executable stack**

   This warning has been recently encountered when compiling the forces example
   on Perlmutter. This does not affect the run, but can be suppressed by adding
   ``-Wl,-znoexecstack`` to the build line.

Additional Information

See the NERSC Perlmutter docs for more information about Perlmutter.