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Showing content from https://github.com/Huite/clipping-benchmarks below:

Huite/clipping-benchmarks: Benchmarking Sutherland-Hodgman clipping in a few languages

This repository contains a basic clipping algorithm for convex polygons implemented in a few languages (Python, Julia, Fortran, C++). Mostly to check how performance of the numba version holds up, but also to compare how much effort is needed to call from Python or Julia. The context is clipping millions of polygons for 2D / 2D - layered unstructured meshes. Volume and area weighted averages require knowing the overlap of one face to the other.

Shapely and pygeos examples are included to check I didn't totally screw up the algorithm, but also illustrate that these GIS routines appear to be rather unsuitable for clipping simple convex polygons in large numbers, as the comparison between shapely and pygeos suggest the Python overhead cost is only a factor ~2; this would suggest most of the time is spent in the algorithm, checking for self-intersection and other degeneracies.

These tests deal with clipping a set of N triangles by another set of N triangles. The coordinates for these triangles are randomly generated, in the x and y intervals [0, 1].

Output of numba -s:

System info:
--------------------------------------------------------------------------------
__Time Stamp__
2020-07-10 09:46:04.439609

__Hardware Information__
Machine                                       : AMD64
CPU Name                                      : skylake
CPU count                                     : 8
CPU Features                                  : 
64bit adx aes avx avx2 bmi bmi2 clflushopt cmov cx16 f16c fma fsgsbase invpcid
lzcnt mmx movbe pclmul popcnt prfchw rdrnd rdseed sahf sgx sse sse2 sse3 sse4.1
sse4.2 ssse3 xsave xsavec xsaveopt xsaves

__OS Information__
Platform                                      : Windows-10-10.0.17134-SP0
Release                                       : 10
System Name                                   : Windows
Version                                       : 10.0.17134
OS specific info                              : 1010.0.17134SP0

In theory, these implementations should work for convex polygons -- not just triangles. However, the area_of_intersection functions currently only take rectangular arrays, so that assumptions has been baked in there. The clip_polygons functions should be able to deal with N vertices (not just 3).

The python environment can be reproduced by running

On Windows, using Visual Studio, it's not entirely straightforward to call ifort from the command line. ifort is available within a specific command prompt, but can be made available by setting the appropriate variables using the ifortvars.bat. In my case:

call "c:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2019.5.281\windows\bin\ifortvars.bat" intel64
ifort -O2 /dll clipping.f90 -o ifort_clipping.dll

To check whether the appropriate exports available, DUMPBIN can be used (instead of *nix nm):

DUMPBIN /exports clipping.dll

If you're not on Windows, maybe add -static-intel, i.e.:

ifort -O2 /dll -static-intel clipping.f90 -o ifort_clipping.dll

Gfortran can be installed via e.g. cygwin. Install MinGW for gfortran, make sure to link libgfortran statically or the DLL will not be accepted. Non-MinGW compilers require the cygwin.dll, so they won't run outside of cygwin on Windows.

gfortran -O2 -shared -static-libgfortran clipping.f90 -o gfortran_clipping.dll 

Use iso_c_bindings to get a consistent name. Gfortran mangles names differently from ifort, which also mangles names differently depending on whether you're on *nix or Windows in terms of upper casing.

A Fortran DLL effectively presents itself as a C library; in Julia it can be called with ccall, and ctypes can be used in Python. This also means that (like in C) you have to pass the shape of the arrays as arguments if you need the shape to be known (this wasn't immediately obvious to me since passing arrays around within Fortran is more straightforward).

Secondly, numpy has numpy.ctypeslib with a couple of helper functions. The argtypes can be set with numpy.ctypeslib.ndpointer. Afterwards, arrays can be passed immediately (and will be checked), which is arguably more convenient and secure than calling numpy.ctypeslib.as_ctypes() for every array in the function call.

Don't give your extension the same name as your .cpp file in the setup.py -- Cython will overwrite it while generating its own .cpp file!

Using C++ std::vector appears to be significantly slower in this case than basic arrays []. Secondly, allocating a new array within clip_polygons is also a good bit slower than pre-allocating it in area_of_intersection. Fortran arrays seem a little nicer, as you can pass a dynamically sized array real(kind=8) :: dimension(:, :) to clip_polygons and it'll be just as fast as (explicitly) pre-allocated.

For C++, at first glance:

• std::vector: ~3.5 times slower
• new array in clip_polygons: ~2 times slower

Pre-allocating subject and output arrays does not seem to make a noticable difference for either Julia or Numba. For numba, it's rather disadvantageous, because numba.prange will not be able to parallellize if the arrays are shared.

Using a stack allocated array as presented here does make a significant difference, from cuttin runtime in half in serial, and much more so in parallel.

import numpy as np
import numba as nb

from numba import types
from numba.extending import intrinsic
from numba.core import cgutils

@intrinsic
def stack_empty(typingctx,size,dtype):
    def impl(context, builder, signature, args):
        ty = context.get_value_type(dtype.dtype)
        ptr = cgutils.alloca_once(builder, ty, size=args[0])
        return ptr
    
    sig = types.CPointer(dtype.dtype)(types.int64,dtype)
    return sig, impl

# Example use:

@nb.njit()
def method_using_intrinsics():
    # Allocate memory on stack
    #arr_ptr can for example be passed to a wrapped c-function
    arr_ptr = stack_empty(10,np.float64)

    # Create a view on the allocated memory and do something
    arr = nb.carray(arr_ptr, 10)
    for i in range(arr.shape[0]):
        arr[i] = i

    print(arr)

The trouble is that the size of the array has to be a compile time constant -- but Numba being JIT, compile time is basically runtime; e.g. use a closure to return a JIT'ed function that allocates the required amount of memory.

In this particular case, Numba does not seem to inline aggressively enough by itself. Setting a decorators to numba.njit(inline="always") results in a factor ~3 speedup.

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