Understanding libEnsemble

Manager, Workers, and User Functions

libEnsemble’s manager allocates work to workers, which perform computations via user functions:

• generator: Generates inputs to the simulator (sim_f)

• simulator: Performs an evaluation based on parameters from the generator (gen_f)

• allocator: Decides whether a simulator or generator should be called (and with what inputs/resources) as workers become available

The default allocator (alloc_f) instructs workers to run the simulator on the highest priority work from the generator. If a worker is idle and there is no work, that worker is instructed to call the generator.

An executor interface is available so user functions can execute and monitor external applications.

libEnsemble uses a NumPy structured array known as the history array to keep a record of all simulations. The global history array is stored on the manager, while selected rows and fields of this array are passed to and from user functions.

Example Use Cases

Below are some expected libEnsemble use cases that we support (or are working to support):

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• A user wants to optimize a simulation calculation. The simulation may already be using parallel resources but not a large fraction of some computer. libEnsemble can coordinate the concurrent evaluation of the simulation sim_f at various parameter values based on candidate parameter values from gen_f (possibly after each sim_f output).

• A user has a gen_f that produces meshes for a sim_f. Given the sim_f output, the gen_f can refine a mesh or produce a new mesh. libEnsemble can ensure that the calculated meshes can be used by multiple simulations without requiring moving data.

• A user wants to evaluate a simulation sim_f with different sets of parameters, each drawn from a set of possible values. Some parameter values are known to cause the simulation to fail. libEnsemble can stop unresponsive evaluations and recover computational resources for future evaluations. The gen_f can possibly update the sampling after discovering regions where evaluations of sim_f fail.

• A user has a simulation sim_f that requires calculating multiple expensive quantities, some of which depend on other quantities. The sim_f can observe intermediate quantities to stop related calculations and preempt future calculations associated with poor parameter values.

• A user has a sim_f with multiple fidelities, with the higher-fidelity evaluations requiring more computational resources, and a gen_f/alloc_f that decides which parameters should be evaluated and at what fidelity level. libEnsemble can coordinate these evaluations without requiring the user to know parallel programming.

• A user wishes to identify multiple local optima for a sim_f. Furthermore, sensitivity analysis is desired at each identified optimum. libEnsemble can use the points from the APOSMM gen_f to identify optima; and after a point is ruled to be an optimum, a different gen_f can produce a collection of parameters necessary for sensitivity analysis of sim_f.

Combinations of these use cases are supported as well. An example of such a combination is using libEnsemble to solve an optimization problem that relies on simulations that fail frequently.

Glossary

Here we define some terms used throughout libEnsemble’s code and documentation. Although many of these terms seem straightforward, defining such terms assists with keeping confusion to a minimum when communicating about libEnsemble and its capabilities.

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• Manager: Single libEnsemble process facilitating communication between other processes. Within libEnsemble, the Manager process configures and passes work to and from the workers.

• Worker: libEnsemble processes responsible for performing units of work, which may include submitting or executing tasks. Worker processes run generation and simulation routines, submit additional tasks for execution, and return results to the manager.

• Calling Script: libEnsemble is typically imported, parameterized, and initiated in a single Python file referred to as a calling script. sim_f and gen_f functions are also commonly configured and parameterized here.

• User function: A generator, simulator, or allocation function. These are Python functions that govern the libEnsemble workflow. They must conform to the libEnsemble API for each respective user function, but otherwise can be created or modified by the user. libEnsemble comes with many examples of each type of user function.

• Executor: The executor can be used within user functions to provide a simple, portable interface for running and managing user tasks (applications). There are multiple executors including the MPIExecutor and BalsamExecutor. The base Executor class allows local sub-processing of serial tasks.

• Submit: Enqueue or indicate that one or more jobs or tasks need to be launched. When using the libEnsemble Executor, a submitted task is executed immediately or queued for execution.

• Tasks: Sub-processes or independent units of work. Workers perform tasks as directed by the manager; tasks may include submitting external programs for execution using the Executor.

• Persistent: Typically, a worker communicates with the manager before and after initiating a user gen_f or sim_f calculation. However, user functions may also be constructed to communicate directly with the manager, for example, to efficiently maintain and update data structures instead of communicating them between manager and worker. These calculations and the workers assigned to them are referred to as persistent.

• Resource Manager libEnsemble has a built-in resource manager that can detect (or be provided with) a set of resources (e.g., a node-list). Resources are divided up among workers (using resource sets) and can be dynamically reassigned.

• Resource Set: The smallest unit of resources that can be assigned (and dynamically reassigned) to workers. By default it is the provisioned resources divided by the number of workers (excluding any workers given in the zero_resource_workers libE_specs option). However, it can also be set directly by the num_resource_sets libE_specs option.

• Slot: The resource sets enumerated on a node (starting with zero). If a resource set has more than one node, then each node is considered to have slot zero.