.. literalinclude:: ../../../numba/tests/doc_examples/test_examples.py :language: python :caption: from ``test_mandelbrot`` of ``numba/tests/doc_examples/test_examples.py`` :start-after: magictoken.ex_mandelbrot.begin :end-before: magictoken.ex_mandelbrot.end :dedent: 12 :linenos:
.. literalinclude:: ../../../numba/tests/doc_examples/test_examples.py :language: python :caption: from ``test_moving_average`` of ``numba/tests/doc_examples/test_examples.py`` :start-after: magictoken.ex_moving_average.begin :end-before: magictoken.ex_moving_average.end :dedent: 12 :linenos:
The code below showcases the potential performance improvement when using the :ref:`nogil <jit-nogil>` feature. For example, on a 4-core machine, the following results were printed:
numpy (1 thread) 145 ms numba (1 thread) 128 ms numba (4 threads) 35 ms
Note
If preferred it's possible to use the standard concurrent.futures module rather than spawn threads and dispatch tasks by hand.
.. literalinclude:: ../../../numba/tests/doc_examples/test_examples.py :language: python :caption: from ``test_no_gil`` of ``numba/tests/doc_examples/test_examples.py`` :start-after: magictoken.ex_no_gil.begin :end-before: magictoken.ex_no_gil.end :dedent: 12 :linenos: