Compiling Python code using LLVM
Numba is an Open Source NumPy-aware optimizing compiler for Python sponsored by Continuum Analytics, Inc. It uses the remarkable LLVM compiler infrastructure to compile Python syntax to machine code.
It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls effectively removing the “interpreter” but not removing the dynamic indirection.
Numba is also not a tracing JIT. It compiles your code before it gets run either using run-time type information or type information you provide in the decorator.
Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy.
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Source Files
| Filename | Size | Changed |
|---|---|---|
| _link | 0000000147 147 Bytes | |
| _multibuild | 0000000154 154 Bytes | |
| numba-0.59.0.tar.gz | 0002656773 2.53 MB | |
| python-numba.changes | 0000067333 65.8 KB | |
| python-numba.spec | 0000006370 6.22 KB | |
| skip-failing-tests.patch | 0000002524 2.46 KB |
Revision 77 (latest revision is 99)
Dirk Mueller (dirkmueller)
committed
(revision 77)
- update to 0.59.0
* Python 3.12 support
* minimum supported version to 3.9
* Add support for ufunc attributes and reduce
* Add a config variable to enable / disable the llvmlite memory
manager
* see https://numba.readthedocs.io/en/stable/release/0.59.0-notes.html#highlights
* fix regressions with 0.57.0
+ Support is added for the dict(iterable) constructor.
- Clean up leftover Python 3.8 gubbins, look forward to Python 3.11 support.
This release focuses on performance improvements, but also adds
some new features and contains numerous bug fixes and stability
* Intel kindly sponsored research and development into producing
a new reference count pruning pass. This pass operates at the
LLVM level and can prune a number of common reference counting
patterns. This will improve performance for two primary
- There will be less pressure on the atomic locks used to do
- Removal of reference counting operations permits more
inlining and the optimisation passes can in general do more
* Intel also sponsored work to improve the performance of the
numba.typed.List container, particularly in the case of
* Superword-level parallelism vectorization is now switched on
and the optimisation pipeline has been lightly analysed and
tuned so as to be able to vectorize more and more often
* The inspect_cfg method on the JIT dispatcher object has been
significantly enhanced and now includes highlighted output and
* The BSD operating system is now unofficially supported (Stuart
* Numerous features/functionality improvements to NumPy support,
- the ndarray allocators, empty, ones and zeros, accepting a
* Cudasim support for mapped array, memcopies and memset has
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