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.
In a given HPC allocation the executorlib 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 executorlib 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 executorlib can be installed without a job scheduler.
The executorlib 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 executorlib library can be employed to distribute a series of python
function calls over the available computing resources to achieve maximum computing resource utilization.
The following examples illustrates how executorlib can be used to distribute a series of MPI parallel function calls
within a queuing system allocation. example.py:
import flux.job
from executorlib 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, executor=flux_exe, resource_dict={"cores": 2}) 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 executorlib 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 executorlib.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.
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
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