Running on HPC Systems

Central v Distributed

libEnsemble has been developed, supported, and tested on systems of highly varying scales, from laptops to thousands of compute nodes. On multi-node systems, there are two basic modes of configuring libEnsemble to run and launch tasks (user applications) on the available nodes.

The first mode we refer to as central mode, where the libEnsemble manager and worker processes are grouped on to one or more dedicated nodes. Workers launch applications onto the remaining allocated nodes:

Alternatively, in distributed mode, the libEnsemble (manager/worker) processes will share nodes with submitted tasks. This enables libEnsemble, using the mpi4py communicator, to be run with the workers spread across nodes so as to be co-located with their tasks.

Configurations with multiple nodes per worker or multiple workers per node are both common use cases. The distributed approach allows the libEnsemble worker to read files produced by the application on local node storage. HPC systems that allow only one application to be launched to a node at any one time prevent distributed configuration.

Configuring the Run

On systems with a job scheduler, libEnsemble is typically run within a single job submission. All user simulations will run on the nodes within that allocation.

How does libensemble know where to run tasks (user applications)?

The libEnsemble Executor can be initialized from the user calling script, and then used by workers to run tasks. The Executor will automatically detect the nodes available on most systems. Alternatively, the user can provide a file called node_list in the run directory. By default, the Executor will divide up the nodes evenly to each worker. If the argument libE_specs["dedicated_mode"]=True is used when initializing libEnsemble, then any node that is running a libEnsemble manager or worker will be removed from the node-list available to the workers, ensuring libEnsemble has dedicated nodes.

To run in central mode using a 5 node allocation with 4 workers. From the head node of the allocation:

mpirun -np 5 python myscript.py

or:

python myscript.py --comms local --nworkers 4

Either of these will run libEnsemble (inc. manager and 4 workers) on the first node. The remaining 4 nodes will be divided amongst the workers for submitted applications. If the same run was performed without libE_specs["dedicated_mode"]=True, runs could be submitted to all 5 nodes. The number of workers can be modified to allow either multiple workers to map to each node or multiple nodes per worker.

To launch libEnsemble distributed requires a less trivial libEnsemble run script. For example:

mpirun -np 5 -ppn 1 python myscript.py

would launch libEnsemble with 5 processes across 5 nodes. However, the manager would have its own node, which is likely wasteful. More often, a machinefile is used to add the manager to the first node. In the examples directory, you can find an example submission script, configured to run libensemble distributed, with multiple workers per node or multiple nodes per worker, and adding the manager onto the first node.

HPC systems that only allow one application to be launched to a node at any one time, will not allow a distributed configuration.

Systems with Launch/MOM nodes

Some large systems have a 3-tier node setup. That is, they have a separate set of launch nodes (known as MOM nodes on Cray Systems). User batch jobs or interactive sessions run on a launch node. Most such systems supply a special MPI runner which has some application-level scheduling capability (eg. aprun, jsrun). MPI applications can only be submitted from these nodes. Examples of these systems include: Summit, Sierra and Theta.

There are two ways of running libEnsemble on these kind of systems. The first, and simplest, is to run libEnsemble on the launch nodes. This is often sufficient if the worker’s simulation or generation functions are not doing much work (other than launching applications). This approach is inherently centralized. The entire node allocation is available for the worker-launched tasks.

However, running libEnsemble on the compute nodes is potentially more scalable and will better manage simulation and generation functions that contain considerable computational work or I/O. Therefore the second option is to use proxy task-execution services like Balsam.

Balsam - Externally managed applications

Running libEnsemble on the compute nodes while still submitting additional applications requires alternative Executors that connect to external services like Balsam. Balsam can take tasks submitted by workers and execute them on the remaining compute nodes, or to entirely different systems.

(New) Multi-System: libEnsemble + BalsamExecutor

Submission scripts for running on launch/MOM nodes and for using Balsam, can be be found in the examples.

Mapping Tasks to Resources

The resource manager can detect system resources, and partition these to workers. The MPI Executor accesses the resources available to the current worker when launching tasks.

Zero-resource workers

Users with persistent gen_f functions may notice that the persistent workers are still automatically assigned system resources. This can be resolved by fixing the number of resource sets.

Overriding Auto-detection

libEnsemble can automatically detect system information. This includes resource information, such as available nodes and the number of cores on the node, and information about available MPI runners.

System detection for resources can be overridden using the resource_info libE_specs option.

When using the MPI Executor, it is possible to override the detected information using the custom_info argument. See the MPI Executor for more.

funcX - Remote User functions

Alternatively to much of the above, if libEnsemble is running on some resource with internet access (laptops, login nodes, other servers, etc.), workers can be instructed to launch generator or simulator user function instances to separate resources from themselves via funcX, a distributed, high-performance function-as-a-service platform:

This is useful for running ensembles across machines and heterogeneous resources, but comes with several caveats:

User functions registered with funcX must be non-persistent, since manager-worker communicators can’t be serialized or used by a remote resource.

Likewise, the Executor.manager_poll() capability is disabled. The only available control over remote functions by workers is processing return values or exceptions when they complete.

funcX imposes a handful of task-rate and data limits on submitted functions.

Users are responsible for authenticating via Globus and maintaining their funcX endpoints on their target systems.

Users can still define Executor instances within their user functions and submit MPI applications normally, as long as libEnsemble and the target application are accessible on the remote system:

# Within remote user function
from libensemble.executors import MPIExecutor
exctr = MPIExecutor()
exctr.register_app(full_path="/home/user/forces.x", app_name="forces")
task = exctr.submit(app_name="forces", num_procs=64)

Specify a funcX endpoint in either sim_specs or gen_specs via the funcx_endpoint key. For example:

sim_specs = {
    "sim_f": sim_f,
    "in": ["x"],
    "out": [("f", float)],
    "funcx_endpoint": "3af6dc24-3f27-4c49-8d11-e301ade15353",
}

See the libensemble/tests/scaling_tests/funcx_forces directory for a complete remote-simulation example.

Instructions for Specific Platforms

The following subsections have more information about configuring and launching libEnsemble on specific HPC systems.