pympipool

Challenges

In high performance computing (HPC) the Python programming language is commonly used as high-level language to orchestrate the coupling of scientific applications. Still the efficient usage of highly parallel HPC clusters remains challenging, in primarily three aspects:

• Communication: Distributing python function calls over hundreds of compute node and gathering the results on a shared file system is technically possible, but highly inefficient. A socket-based communication approach is preferable.
• Resource Management: Assigning Python functions to GPUs or executing Python functions on multiple CPUs using the message passing interface (MPI) requires major modifications to the python workflow.
• Integration: Existing workflow libraries implement a secondary the job management on the Python level rather than leveraging the existing infrastructure provided by the job scheduler of the HPC.

pympipool is ...

In a given HPC allocation the pympipool library addresses these challenges by extending the Executor interface of the standard Python library to support the resource assignment in the HPC context. Computing resources can either be assigned on a per function call basis or as a block allocation on a per Executor basis. The pympipool library is built on top of the flux-framework to enable fine-grained resource assignment. In addition, Simple Linux Utility for Resource Management (SLURM) is supported as alternative queuing system and for workstation installations pympipool can be installed without a job scheduler.

pympipool is not ...

The pympipool library is not designed to request an allocation from the job scheduler of an HPC. Instead within a given allocation from the job scheduler the pympipool library can be employed to distribute a series of python function calls over the available computing resources to achieve maximum computing resource utilization.

Example

The following examples illustrates how pympipool can be used to distribute a series of MPI parallel function calls within a queuing system allocation. example.py:

import flux.job
from pympipool import Executor

def calc(i):
    from mpi4py import MPI
    size = MPI.COMM_WORLD.Get_size()
    rank = MPI.COMM_WORLD.Get_rank()
    return i, size, rank

with flux.job.FluxExecutor() as flux_exe:
    with Executor(max_cores=2, cores_per_worker=2, executor=flux_exe) as exe:
        fs = exe.submit(calc, 3)
        print(fs.result())

This example can be executed using:

python example.py

Which returns:

>>> [(0, 2, 0), (0, 2, 1)], [(1, 2, 0), (1, 2, 1)]

The important part in this example is that mpi4py is only used in the calc() function, not in the python script, consequently it is not necessary to call the script with mpiexec but instead a call with the regular python interpreter is sufficient. This highlights how pympipool allows the users to parallelize one function at a time and not having to convert their whole workflow to use mpi4py. The same code can also be executed inside a jupyter notebook directly which enables an interactive development process.

The interface of the standard concurrent.futures.Executor is extended by adding the option cores_per_worker=2 to assign multiple MPI ranks to each function call. To create two workers the maximum number of cores can be increased to max_cores=4. In this case each worker receives two cores resulting in a total of four CPU cores being utilized.

After submitting the function calc() with the corresponding parameter to the executor exe.submit(calc, 0) a python concurrent.futures.Future is returned. Consequently, the pympipool.Executor can be used as a drop-in replacement for the concurrent.futures.Executor which allows the user to add parallelism to their workflow one function at a time.

Disclaimer

While we try to develop a stable and reliable software library, the development remains a opensource project under the BSD 3-Clause License without any warranties::

BSD 3-Clause License

Copyright (c) 2022, Jan Janssen
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

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Documentation

• Installation
  • Compatible Job Schedulers
  • pympipool with Flux Framework
  • Test Flux Framework
  • Without Flux Framework
• Examples
  • Compatibility
  • Resource Assignment
  • Data Handling
  • Up-Scaling
  • Coupled Functions
  • SLURM Job Scheduler
  • Workstation Support
• Development
  • Contributions
  • License
  • Integration