IronPython vs Python speed with numpy

Question

I have some Python source code that manipulates lists of lists of numbers (say about 10,000 floating point numbers) and does various calculations on these numbers, including a lot of numpy.linalg.norm for example.

Run time had not been an issue until we recently started using this code from a C# UI (running this Python code from C# via IronPython). I extracted a set of function calls (doing things as described in the 1st paragraph) and found that this code takes about 4x longer to run in IronPython compared to Python 2.7 (and this is after excluding the startup/setup time in C#/IronPython). I'm using a C# stopwatch around the repeated IronPython calls from C# and using the timeit module around an execfile in Python 2.7 (so the Python time results include more operation like loading the file, creating the objects, ... whereas the C# doesn't). The former requires about 4.0 seconds while the latter takes about 0.9 seconds.

Would you expect this kind of difference? Any ideas how I might resolve this issue? Other comments?

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Edit:

Here is a simple example of code that runs about 10x slower on IronPython on my machine (4 seconds in Python 2.7 and 40 seconds in IronPython):

n = 700
for i in range(n-1):
    for j in range(i, n):
        dist = np.linalg.norm(np.array([i, i, i]) - np.array([j, j, j]))

Answer 1

You're using NUMPY?! You're lucky it works in IronPython at all! The support is being added literally as we speak!

To be exact, there's a CPython-extension-to-IronPython interface project and there's a native CLR port or numpy. I dunno which one you're using but both ways are orders of magnitude slower that working with the C version in CPython.

UPDATE:

The Scipy for IronPython port by Enthought that you're apparently using looks abandoned: last commits in the repos linked are a few years old and it's missing from http://www.scipy.org/install.html, too. Judging by the article, it was a partial port with interface in .NET and core in C linked with a custom interface. The previous paragraph stands for it, too.

Using the information from Faster alternatives to numpy.argmax/argmin which is slow , you may get some speedup if you limit data passing back and forth between the CLR and the C core.