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Numba
{:.no_toc}
* TOC {:toc}The goal
Questions to David Rotermund
pip install numba
Numba basic types
For the example that will show you the options of optimization we need to understand the numba naming schema.
Numbers
For the function signatures we need to be able to translate the usual np.dtype into numpy.types.
For doing so we just replace np. by numba.types. .
| Type name(s) | Shorthand | Comments |
|---|---|---|
| numba.types.boolean | b1 | represented as a byte |
| numba.types.uint8, byte | u1 | 8-bit unsigned byte |
| numba.types.uint16 | u2 | 16-bit unsigned integer |
| numba.types.uint32 | u4 | 32-bit unsigned integer |
| numba.types.uint64 | u8 | 64-bit unsigned integer |
| numba.types.int8, char | i1 | 8-bit signed byte |
| numba.types.int16 | i2 | 16-bit signed integer |
| numba.types.int32 | i4 | 32-bit signed integer |
| numba.types.int64 | i8 | 64-bit signed integer |
| numba.types.intc | – | C int-sized integer |
| numba.types.uintc | – | C int-sized unsigned integer |
| numba.types.intp | – | pointer-sized integer |
| numba.types.uintp | – | pointer-sized unsigned integer |
| numba.types.float32 | f4 | single-precision floating-point number |
| numba.types.float64, double | f8 | double-precision floating-point number |
| numba.types.complex64 | c8 | single-precision complex number |
| numba.types.complex128 | c16 | double-precision complex number |
Arrays
If we have arrays in the function signature, which is a very likely senario, we might want to give as much information to numpy as possible about the numpy.ndarray. In some cases it is very benificial to make a np.ndarray an array with C memory layout and tell numba about it.
We can use the numpy function numpy.ascontiguousarray for converting a numpy array into a C memory layout.
We can also check a numpy array, let's call it X, if it is already in the C memory layout. This is done by looking at X.flags['C_CONTIGUOUS'].
Some example for array signatures are:
| numba.types.float32[:] | 1d array of float32 with no particular memory layout |
| numba.types.float32[:,:] | 2d array of float32 with no particular memory layout |
| numba.types.float32[:,:,:] | 3d array of float32 with no particular memory layout |
| numba.types.float32[::1] | 1d array of float32 with C memory layout |
| numba.types.float32[:,::1] | 2d array of float32 with C memory layout |
| numba.types.float32[:,:,::1] | 3d array of float32 with C memory layout |
| numba.types.float32[::1,:] | 2d array of float32 with Fortran memory layout |
| numba.types.float32[::1,:,:] | 3d array of float32 with Fortran memory layout |