Executor Overview

A typical libEnsemble workflow will include launching tasks from a sim_f (or gen_f) running on a worker. We use “task” to represent an application submission by libEnsemble to the system, which may be the compute nodes of a supercomputer, cluster, or other compute resource.

An Executor interface is provided by libEnsemble to remove the burden of system interaction from the user and ease the writing of portable user scripts that launch applications. The Executor provides the key functions: submit(), poll(), wait(), and kill(). Task attributes can be queried to determine the status following each of these commands. Functions are also provided to access and interrogate files in the task’s working directory.

The main Executor class is an abstract class and is inherited by the MPIExecutor, for direct running of MPI applications. We also provide a BalsamMPIExecutor, which submits an MPI run request from a worker running on a compute node to a Balsam service running on a launch node (suitable for systems that do not allow running MPI applications directly from compute nodes).

In a calling script, an Executor object is created, and the executable generator or simulation applications are registered to it for submission. If an alternative Executor like Balsam is used, then the applications can be registered as in the example below. Once in the user-side worker code (sim/gen func), an MPI-based Executor can be retrieved without any need to specify the type.

Once the Executor is retrieved, tasks can be submitted by specifying the app_name from registration in the calling script alongside other optional parameters described in the API. A corresponding Task object instance is returned. As can be seen in the examples below, a variety of Executor and Task attributes and methods can be queried to effectively manage currently running applications within user functions.

Example usage (code runnable with or without a Balsam 0.5.0 backend):

In calling function:

sim_app = '/path/to/my/exe'
USE_BALSAM = False

if USE_BALSAM:
    from libensemble.executors.balsam_executor import BalsamMPIExecutor
    exctr = BalsamMPIExecutor()
else:
    from libensemble.executors.mpi_executor import MPIExecutor
    exctr = MPIExecutor()

exctr.register_app(full_path=sim_app, app_name='sim1')

Note

The Executor set up in the calling script is stored as a class attribute and does not have to be passed to libE. It is extracted via Executor.executor in the sim function (regardless of type).

In user simulation function:

import time
from libensemble.executors.executor import Executor

# Will return Executor (whether MPI or inherited such as Balsam).
exctr = Executor.executor

task = exctr.submit(app_name='sim1', num_procs=8, app_args='input.txt',
                    stdout='out.txt', stderr='err.txt')

timeout_sec = 600
poll_delay_sec = 1

while(not task.finished):

    # Has manager sent a finish signal
    exctr.manager_poll()
    if exctr.manager_signal == 'finish':
        task.kill()
        my_cleanup()

    # Check output file for error and kill task
    elif task.stdout_exists():
        if 'Error' in task.read_stdout():
            task.kill()

    elif task.runtime > timeout_sec:
        task.kill()  # Timeout

    else:
        time.sleep(poll_delay_sec)
        task.poll()

print(task.state)  # state may be finished/failed/killed

Users primarily concerned with running their tasks to completion without intermediate evaluation don’t necessarily need to construct a polling loop like above, but can instead use an Executor instance’s polling_loop() method. An alternative to the above simulation function may resemble:

import time
from libensemble.executors.executor import Executor

# Will return Executor (whether MPI or inherited such as Balsam).
exctr = Executor.executor

task = exctr.submit(app_name='sim1', num_procs=8, app_args='input.txt',
                    stdout='out.txt', stderr='err.txt')

timeout_sec = 600
poll_delay_sec = 1

exctr.polling_loop(task, timeout=timeout_sec, delay=poll_delay_sec)

print(task.state)  # state may be finished/failed/killed

See the executor interface for the complete API.

For a more realistic example see the Electrostatic Forces example, which launches the forces.x application as an MPI task.

Note

Applications or tasks submitted via the Balsam Executor are referred to as “jobs” within Balsam, including within Balsam’s database and when describing the state of a completed submission.

The MPIExecutor autodetects system criteria such as the appropriate MPI launcher and mechanisms to poll and kill tasks. It also has access to the resource manager, which partitions resources amongst workers, ensuring that runs utilize different resources (e.g., nodes). Furthermore, the MPIExecutor offers resilience via the feature of re-launching tasks that fail to start because of system factors.

Various back-end mechanisms may be used by the Executor to best interact with each system, including proxy launchers or task management systems such as Balsam. Currently, these Executors launch at the application level within an existing resource pool. However, submissions to a batch scheduler may be supported in future Executors.

See Running on HPC Systems to see, with diagrams, how common Executor options such as dedicated_mode affect the run configuration on clusters and supercomputers.