threadpoolctl 3.2.0

threadpoolctl

Thread-pool Controls

Python helpers to limit the number of threads used in the threadpool-backed of common native libraries used for scientific computing and data science (e.g. BLAS and OpenMP).

Fine control of the underlying thread-pool size can be useful in workloads that involve nested parallelism so as to mitigate oversubscription issues.

Installation

• For users, install the last published version from PyPI:

  pip install threadpoolctl
  

• For contributors, install from the source repository in developer mode:

  pip install -r dev-requirements.txt
  flit install --symlink
  

  then you run the tests with pytest:

  pytest
  

Usage

Command Line Interface

Get a JSON description of thread-pools initialized when importing python packages such as numpy or scipy for instance:

python -m threadpoolctl -i numpy scipy.linalg
[
  {
    "filepath": "/home/ogrisel/miniconda3/envs/tmp/lib/libmkl_rt.so",
    "prefix": "libmkl_rt",
    "user_api": "blas",
    "internal_api": "mkl",
    "version": "2019.0.4",
    "num_threads": 2,
    "threading_layer": "intel"
  },
  {
    "filepath": "/home/ogrisel/miniconda3/envs/tmp/lib/libiomp5.so",
    "prefix": "libiomp",
    "user_api": "openmp",
    "internal_api": "openmp",
    "version": null,
    "num_threads": 4
  }
]

The JSON information is written on STDOUT. If some of the packages are missing, a warning message is displayed on STDERR.

Python Runtime Programmatic Introspection

Introspect the current state of the threadpool-enabled runtime libraries that are loaded when importing Python packages:

>>> from threadpoolctl import threadpool_info
>>> from pprint import pprint
>>> pprint(threadpool_info())
[]

>>> import numpy
>>> pprint(threadpool_info())
[{'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libmkl_rt.so',
  'internal_api': 'mkl',
  'num_threads': 2,
  'prefix': 'libmkl_rt',
  'threading_layer': 'intel',
  'user_api': 'blas',
  'version': '2019.0.4'},
 {'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libiomp5.so',
  'internal_api': 'openmp',
  'num_threads': 4,
  'prefix': 'libiomp',
  'user_api': 'openmp',
  'version': None}]

>>> import xgboost
>>> pprint(threadpool_info())
[{'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libmkl_rt.so',
  'internal_api': 'mkl',
  'num_threads': 2,
  'prefix': 'libmkl_rt',
  'threading_layer': 'intel',
  'user_api': 'blas',
  'version': '2019.0.4'},
 {'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libiomp5.so',
  'internal_api': 'openmp',
  'num_threads': 4,
  'prefix': 'libiomp',
  'user_api': 'openmp',
  'version': None},
 {'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libgomp.so.1.0.0',
  'internal_api': 'openmp',
  'num_threads': 4,
  'prefix': 'libgomp',
  'user_api': 'openmp',
  'version': None}]

In the above example, numpy was installed from the default anaconda channel and comes with MKL and its Intel OpenMP (libiomp5) implementation while xgboost was installed from pypi.org and links against GNU OpenMP (libgomp) so both OpenMP runtimes are loaded in the same Python program.

The state of these libraries is also accessible through the object oriented API:

>>> from threadpoolctl import ThreadpoolController, threadpool_info
>>> from pprint import pprint
>>> import numpy
>>> controller = ThreadpoolController()
>>> pprint(controller.info())
[{'architecture': 'Haswell',
  'filepath': '/home/jeremie/miniconda/envs/dev/lib/libopenblasp-r0.3.17.so',
  'internal_api': 'openblas',
  'num_threads': 4,
  'prefix': 'libopenblas',
  'threading_layer': 'pthreads',
  'user_api': 'blas',
  'version': '0.3.17'}]

>>> controller.info() == threadpool_info()
True

Setting the Maximum Size of Thread-Pools

Control the number of threads used by the underlying runtime libraries in specific sections of your Python program:

>>> from threadpoolctl import threadpool_limits
>>> import numpy as np

>>> with threadpool_limits(limits=1, user_api='blas'):
...     # In this block, calls to blas implementation (like openblas or MKL)
...     # will be limited to use only one thread. They can thus be used jointly
...     # with thread-parallelism.
...     a = np.random.randn(1000, 1000)
...     a_squared = a @ a

The threadpools can also be controlled via the object oriented API, which is especially useful to avoid searching through all the loaded shared libraries each time. It will however not act on libraries loaded after the instantiation of the ThreadpoolController:

>>> from threadpoolctl import ThreadpoolController
>>> import numpy as np
>>> controller = ThreadpoolController()

>>> with controller.limit(limits=1, user_api='blas'):
...     a = np.random.randn(1000, 1000)
...     a_squared = a @ a

Restricting the limits to the scope of a function

threadpool_limits and ThreadpoolController can also be used as decorators to set the maximum number of threads used by the supported libraries at a function level. The decorators are accessible through their wrap method:

>>> from threadpoolctl import ThreadpoolController, threadpool_limits
>>> import numpy as np
>>> controller = ThreadpoolController()

>>> @controller.wrap(limits=1, user_api='blas')
... # or @threadpool_limits.wrap(limits=1, user_api='blas')
... def my_func():
...     # Inside this function, calls to blas implementation (like openblas or MKL)
...     # will be limited to use only one thread.
...     a = np.random.randn(1000, 1000)
...     a_squared = a @ a
...

Writing a custom library controller

Currently, threadpoolctl has support for OpenMP and the main BLAS libraries. However it can also be used to control the threadpool of other native libraries, provided that they expose an API to get and set the limit on the number of threads. For that, one must implement a controller for this library and register it to threadpoolctl.

A custom controller must be a subclass of the LibController class and implement the attributes and methods described in the docstring of LibController. Then this new controller class must be registered using the threadpoolctl.register function. An complete example can be found here.

Sequential BLAS within OpenMP parallel region

When one wants to have sequential BLAS calls within an OpenMP parallel region, it's safer to set limits="sequential_blas_under_openmp" since setting limits=1 and user_api="blas" might not lead to the expected behavior in some configurations (e.g. OpenBLAS with the OpenMP threading layer https://github.com/xianyi/OpenBLAS/issues/2985).

Known Limitations

• threadpool_limits can fail to limit the number of inner threads when nesting parallel loops managed by distinct OpenMP runtime implementations (for instance libgomp from GCC and libomp from clang/llvm or libiomp from ICC).

  See the test_openmp_nesting function in tests/test_threadpoolctl.py for an example. More information can be found at: https://github.com/jeremiedbb/Nested_OpenMP

  Note however that this problem does not happen when threadpool_limits is used to limit the number of threads used internally by BLAS calls that are themselves nested under OpenMP parallel loops. threadpool_limits works as expected, even if the inner BLAS implementation relies on a distinct OpenMP implementation.

• Using Intel OpenMP (ICC) and LLVM OpenMP (clang) in the same Python program under Linux is known to cause problems. See the following guide for more details and workarounds: https://github.com/joblib/threadpoolctl/blob/master/multiple_openmp.md

• Setting the maximum number of threads of the OpenMP and BLAS libraries has a global effect and impacts the whole Python process. There is no thread level isolation as these libraries do not offer thread-local APIs to configure the number of threads to use in nested parallel calls.

Maintainers

To make a release:

Bump the version number (__version__) in threadpoolctl.py.

Build the distribution archives:

pip install flit
flit build

Check the contents of dist/.

If everything is fine, make a commit for the release, tag it, push the tag to github and then:

flit publish

Credits

The initial dynamic library introspection code was written by @anton-malakhov for the smp package available at https://github.com/IntelPython/smp .

threadpoolctl extends this for other operating systems. Contrary to smp, threadpoolctl does not attempt to limit the size of Python multiprocessing pools (threads or processes) or set operating system-level CPU affinity constraints: threadpoolctl only interacts with native libraries via their public runtime APIs.