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tomMoral merged 6 commits into from
Jun 18, 2026
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DOC minimally discuss inconsistencies in openMP's limitation thread locality #209

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itamarst Jun 17, 2026
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27 changes: 23 additions & 4 deletions README.md
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Expand Up @@ -8,6 +8,20 @@ Fine control of the underlying thread-pool size can be useful in
workloads that involve nested parallelism so as to mitigate
oversubscription issues.

> **Important:** In its current state, `threadpoolctl` is only designed for
> situations where BLAS/OpenMP are called from the main thread. Once you start
> calling BLAS or OpenMP from a Python thread pool, the impact of the
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> `threadpoolctl` limiting APIs will be very inconsistent.
>
> Examples where it will work fine:
>
> * When you're using it to configure a worker in a process pool (as long as the
> workers don't starts their own Python thread pool.)
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> * A Jupyter notebook, again so long as you don't call BLAS/OpenMP from a
> Python thread pool.
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>
> For more details and a plan to fix this, see https://github.com/joblib/threadpoolctl/issues/208

## Installation

- For users, install the last published version from PyPI:
Expand Down Expand Up @@ -322,11 +336,16 @@ https://github.com/xianyi/OpenBLAS/issues/2985).
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.
- Setting the maximum number of threads of the OpenMP and BLAS libraries has
inconsistent scope (thread-local vs process-wide) and semantics (thread-local
vs process-wide) depending on the underlying library. For more details see
https://github.com/joblib/threadpoolctl/issues/208
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For example, if you're using OpenMP with libgomp (gcc) or libomp (clang), the
setting is thread-local and sets how many OpenMP threads will be started in
the current thread. On the other hand, with OpenBLAS with pthreads backend or
on Windows, the setting is process-wide and impacts the size of a process-wide
thread pool shared across all threads in the process.

## Maintainers

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