Release Notes

Version 0.12

Version 0.12 contains a big refactor of the compiler. The main objective for this refactor was to simplify the code base to create a better foundation for further work. A secondary objective was to improve the worst case performance to ensure that compiled functions in object mode never run slower than pure Python code (this was a problem in several cases with the old code base). This refactor is still a work in progress and further testing is needed.

Main improvements:

• Major refactor of compiler for performance and maintenance reasons
• Better fallback to object mode when native mode fails
• Improved worst case performance in object mode

The public interface of numba has been slightly changed. The idea is to make it cleaner and more rational:

• jit decorator has been modified, so that it can be called without a signature. When called without a signature, it behaves as the old autojit. Autojit has been deprecated in favour of this approach.
• Jitted functions can now be overloaded.
• Added a “njit” decorator that behaves like “jit” decorator with nopython=True.
• The numba.vectorize namespace is gone. The vectorize decorator will be in the main numba namespace.
• Added a guvectorize decorator in the main numba namespace. It is similiar to numba.vectorize, but takes a dimension signature. It generates gufuncs. This is a replacement for the GUVectorize gufunc factory which has been deprecated.

Main regressions (will be fixed in a future release):

• Creating new NumPy arrays is not supported in nopython mode
• Returning NumPy arrays is not supported in nopython mode
• NumPy array slicing is not supported in nopython mode
• lists and tuples are not supported in nopython mode
• string, datetime, cdecimal, and struct types are not implemented yet
• Extension types (classes) are not supported in nopython mode
• Closures are not supported
• Raise keyword is not supported
• Recursion is not support in nopython mode

Version 0.11

• Experimental support for NumPy datetime type

Version 0.10

• Annotation tool (./bin/numba –annotate –fancy) (thanks to Jay Bourque)
• Open sourced prange
• Support for raise statement
• Pluggable array representation
• Support for enumerate and zip (thanks to Eugene Toder)
• Better string formatting support (thanks to Eugene Toder)
• Builtins min(), max() and bool() (thanks to Eugene Toder)
• Fix some code reloading issues (thanks to Björn Linse)
• Recognize NumPy scalar objects (thanks to Björn Linse)

Version 0.9

• Improved math support
• Open sourced generalized ufuncs
• Improved array expressions

Version 0.8

• Support for autojit classes

  • Inheritance not yet supported

• Python 3 support for pycc

• Allow retrieval of ctypes function wrapper

  • And hence support retrieval of a pointer to the function

• Fixed a memory leak of array slicing views

Version 0.7.2

• Official Python 3 support (python 3.2 and 3.3)
• Support for intrinsics and instructions
• Various bug fixes (see https://github.com/numba/numba/issues?milestone=7&state=closed)

Version 0.7.1

• Various bug fixes

Version 0.7

• Open sourced single-threaded ufunc vectorizer

• Open sourced NumPy array expression compilation

• Open sourced fast NumPy array slicing

• Experimental Python 3 support

• Support for typed containers

  • typed lists and tuples

• Support for iteration over objects

• Support object comparisons

• Preliminary CFFI support

  • Jit calls to CFFI functions (passed into autojit functions)
  • TODO: Recognize ffi_lib.my_func attributes

• Improved support for ctypes

• Allow declaring extension attribute types as through class attributes

• Support for type casting in Python

  • Get the same semantics with or without numba compilation

• Support for recursion

  • For jit methods and extension classes

• Allow jit functions as C callbacks

• Friendlier error reporting

• Internal improvements

• A variety of bug fixes

Version 0.6.1

• Support for bitwise operations

Version 0.6

• Python 2.6 support

• Programmable typing

  • Allow users to add type inference for external code

• Better NumPy type inference

  • outer, inner, dot, vdot, tensordot, nonzero, where, binary ufuncs + methods (reduce, accumulate, reduceat, outer)

• Type based alias analysis

  • Support for strict aliasing

• Much faster autojit dispatch when calling from Python

• Faster numerical loops through data and stride pre-loading

• Integral overflow and underflow checking for conversions from objects

• Make Meta dependency optional

Version 0.5

• SSA-based type inference

  • Allows variable reuse
  • Allow referring to variables before lexical definition

• Support multiple comparisons

• Support for template types

• List comprehensions

• Support for pointers

• Many bug fixes

• Added user documentation

Version 0.4

Version 0.3.2

• Add support for object arithmetic (issue 56).
• Bug fixes (issue 55).

Version 0.3

• Changed default compilation approach to ast
• Added support for cross-module linking
• Added support for closures (can jit inner functions and return them) (see examples/closure.py)
• Added support for dtype structures (can access elements of structure with attribute access) (see examples/structures.py)
• Added support for extension types (numba classes) (see examples/numbaclasses.py)
• Added support for general Python code (use nopython to raise an error if Python C-API is used to avoid unexpected slowness because of lack of implementation defaulting to generic Python)
• Fixed many bugs
• Added support to detect math operations.
• Added with python and with nopython contexts
• Added more examples

Many features need to be documented still. Look at examples and tests for more information.

Version 0.2

• Added an ast approach to compilation
• Removed d, f, i, b from numba namespace (use f8, f4, i4, b1)
• Changed function to autojit2
• Added autojit function to decorate calls to the function and use types of the variable to create compiled versions.
• changed keyword arguments to jit and autojit functions to restype and argtypes to be consistent with ctypes module.
• Added pycc – a python to shared library compiler