Parallel Manager


The parallel manager is a high level wrapper designed for the parameter sweep tool that allows the use of different parallel paradigms for running parameter sweep in parallel, for example, on a supercomputer, using a unified API. Currently, supported parallel backends include:

  1. MPI

  2. Multiprocessing

  3. Concurrent Futures

  4. Ray Core

Parallel manager was implemented to support different workflows of WaterTAP users and developers while reducing the burden to develop software. In particular, it supports collective operations using the various paradigms. These operations are listed below:

  1. Barrier : Waits until all parallel workers arrive at the same point in code

  2. Gather : Gathers an array or list on one or all parallel workers.

  3. Scatter : Divides up the array or list across all the parallel workers

  4. Broadcast : Sends the array or list to all parallel workers

  5. Reduce : Collects the data from all parallel workers and performs a mathematical operation. The current behavior sums the values from all workers.


End User

If the user is simply invoking the parameter sweep tool, they only need to specify parallel backend by using the keyword argument parallel_back_end when constructing a parameter sweep object. Valid keyword values are

  1. “MPI” : For use on distributed memory systems such as a cluster or supercomputer. We have run the parameter sweep on 100 compute nodes with 10400 total workers.

  2. “ConcurrentFutures” : For use on shared memory systems (e.g. your workstation) with flow sheets that initialize quickly.

  3. “MultiProcessing” : For use on shared memory systems with flow sheets that take a long time to initialize.

  4. “RayIo” : For distributed memory systems where MPI is not stable, or hybrid computing.

If any other keyword is provided, the parallel manager reverts to serial. Note that, when using MPI, the end user must still use the mpiexec or mpirun command when executing the parameter sweep from the command line.

Adding Parallel Manager to Your Code

In order to use the parallel manager in your code, you will need to adapt the following statement.

from import create_parallel_manager
parallel_manager = create_parallel_manager(

The above code return 1 of 5 parallel manager classes. There are currently 5 parallel manager classes:

  1. SingleProcessParallelManager : To enable serial implementation

  2. MPIParallelManager : For MPI

  3. ConcurrentFuturesParallelManager : For python concurrent futures

  4. MultiprocessingParallelManager : For more advanced python multiprocessing

  5. RayIoParallelManager : For using Ray

Alternatively, the user can specify the parameter sweep class directly, for example

from import SingleProcessParallelManager
s_parallel_manager = create_parallel_manager(

Each of the parallel manager classes inherits from the base ParallelManager class and defines the abstract methods. The methods can be found in the class documentation below.

Adding Features to the Parallel Manager

An advanced user may add features as necessary to the parallel manager. We ask that they follow appropriate code development practices and include testing and documentation. A lot of features are currently missing from the parallel manager that limits its wider use. We encourage developers to make PRs to the parallel manager.