Bebop is a Cray CS400 cluster with Intel Broadwell and Knights Landing compute nodes available in the Laboratory Computing Resources Center (LCRC) at Argonne National Laboratory.

Configuring Python

Begin by loading the Python 3 Anaconda module:

module load anaconda3

Create a conda virtual environment in which to install libEnsemble and all dependencies:

conda config --add channels intel
conda create --name my_env intelpython3_core python=3
source activate my_env

Installing libEnsemble and Dependencies

You should have an indication that the virtual environment is activated. Start by installing mpi4py in this environment, making sure to reference the preinstalled Intel MPI compiler. Your prompt should be similar to the following block:

(my_env) user@login:~$ CC=mpiicc MPICC=mpiicc pip install mpi4py --no-binary mpi4py

libEnsemble can then be installed via pip or conda. To install via pip:

(my_env) user@login:~$ pip install libensemble

To install via conda:

(my_env) user@login:~$ conda config --add channels conda-forge
(my_env) user@login:~$ conda install -c conda-forge libensemble

See here for more information on advanced options for installing libEnsemble.

Job Submission

Bebop uses Slurm for job submission and management. The two commands you’ll likely use the most to run jobs are srun and sbatch for running interactively and batch, respectively.

libEnsemble node-worker affinity is especially flexible on Bebop. By adjusting srun runtime options users may assign multiple libEnsemble workers to each allocated node(oversubscription) or assign multiple nodes per worker.

Interactive Runs

You can allocate four Knights Landing nodes for thirty minutes through the following:

salloc -N 4 -p knl -A [username OR project] -t 00:30:00

With your nodes allocated, queue your job to start with four MPI ranks:

srun -n 4 python

mpirun should also work. This line launches libEnsemble with a manager and three workers to one allocated compute node, with three nodes available for the workers to launch calculations with the Executor or a launch command. This is an example of running in centralized mode, and, if using the Executor, it should be initiated with central_mode=True


When performing a distributed MPI libEnsemble run and not oversubscribing, specify one more MPI process than the number of allocated nodes. The manager and first worker run together on a node.

If you would like to interact directly with the compute nodes via a shell, the following starts a bash session on a Knights Landing node for thirty minutes:

srun --pty -A [username OR project] -p knl -t 00:30:00 /bin/bash


You will need to reactivate your conda virtual environment and reload your modules! Configuring this routine to occur automatically is recommended.

Batch Runs

Batch scripts specify run settings using #SBATCH statements. A simple example for a libEnsemble use case running in distributed MPI mode on Broadwell nodes resembles the following:

 2#SBATCH -J myjob
 3#SBATCH -N 4
 4#SBATCH -p bdwall
 5#SBATCH -A myproject
 6#SBATCH -o myjob.out
 7#SBATCH -e myjob.error
 8#SBATCH -t 00:15:00
10# These four lines construct a machinefile for the executor and slurm
11srun hostname | sort -u > node_list
12head -n 1 node_list > machinefile.$SLURM_JOBID
13cat node_list >> machinefile.$SLURM_JOBID
14export SLURM_HOSTFILE=machinefile.$SLURM_JOBID
16srun --ntasks 5 python

With this saved as, allocating, configuring, and running libEnsemble on Bebop is achieved by running


Example submission scripts for running on Bebop in distributed and centralized mode are also given in the examples.

Debugging Strategies

View the status of your submitted jobs with squeue, and cancel jobs with scancel <Job ID>.

Additional Information

See the LCRC Bebop docs here for more information about Bebop.