Evaluate using Codon for static compiling

[MicahGale]

Github: https://github.com/exaloop/codon

And of course MIT news: https://news.mit.edu/2023/codon-python-based-compiler-achieve-orders-magnitude-speedups-0314

[MicahGale]

So initial results:

You need to write a codon script AFAIK in python-like syntax and then run/compile that.

I got one test case working in test.codon

from python import mcnpy
from python import numpy as np
from python import time

times = []
for i in range(10):
        start = time.time()
        problem = mcnpy.read_input("big_model.imcnp")
        end = time.time()
        times.append(end - start)

print("max", max(times), "min", min(times))

print("average", np.mean(times), "std dev", np.std(times))

I also ran a similar thing in ipython.

Results from codon:

max 14.552344560623169 min 12.914937257766724
average 13.605058789253235 std dev 0.57067858954262

results from ipython

max 10.400793075561523 min 7.664640665054321 
average 9.345700883865357  std dev 0.7869787794326515

This confirms what @tjlaboss found:

Many of the functions that lack Codon-native implementations (e.g. I/O or OS related functions) will generally also not see substantial speedups from Codon.

[MicahGale]

Important reference: https://docs.exaloop.io/codon/interoperability/python

[MicahGale]

In GitLab by @tjlaboss on Mar 15, 2023, 18:45

I don't think Codon works like that anyway; MCNPy itself would need to be written in Codon or make use of the @codon.jit decorator. from python import mcnpy would still be using Python for mcnpy.

[MicahGale]

Ohhhh.

[MicahGale]

Have you found a way to compile a module?