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Add note about performance of typed containers from the interpreter. #6440

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merged 3 commits into from Feb 2, 2021
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Add note about performance of typed containers from the interpreter. #6440

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Add note about performance of typed containers from the interpreter.
stuartarchibald Nov 2, 2020
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stuartarchibald Nov 16, 2020
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Update docs/source/user/faq.rst
stuartarchibald Dec 2, 2020
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14 changes: 12 additions & 2 deletions docs/source/user/faq.rst
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Expand Up @@ -102,8 +102,8 @@ Does Numba vectorize array computations (SIMD)?
Numba doesn't implement such optimizations by itself, but it lets LLVM
apply them.

Why my loop is not vectorized?
------------------------------
Why has my loop not vectorized?
-------------------------------

Numba enables the loop-vectorize optimization in LLVM by default.
While it is a powerful optimization, not all loops are applicable.
Expand Down Expand Up @@ -150,6 +150,16 @@ In this case, vectorization is rejected because the vectorized code may behave
differently. This is a case to try turning on ``fastmath=True`` to allow
fastmath instructions.

Why are the ``typed`` containers slower when used from the interpreter?
-----------------------------------------------------------------------

The Numba ``typed`` containers found in ``numba.typed`` e.g.
``numba.typed.List`` store their data in an efficient form for access from JIT
compiled code. When these containers are used from the CPython interpreter, the
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data involved has to be converted from/to the container format. This process is
relatively costly and as a result impacts performance. In JIT compiled code no
such penalty exists and so operations on the containers are much quicker and
often faster than the pure Python equivalent.

Does Numba automatically parallelize code?
------------------------------------------
Expand Down