Fastest way to apply arithmetic operations to System.Array in IronPython

Question

I would like to add (arithmetics) two large System.Arrays element-wise in IronPython and store the result in the first array like this:

    for i in range(0:ArrA.Count) :
      arrA.SetValue(i, arrA.GetValue(i) + arrB.GetValue(i));

However, this seems very slow. Having a C background I would like to use pointers or iterators. However, I do not know how I should apply the IronPython idiom in a fast way. I cannot use Python lists, as my objects are strictly from type System.Array. The type is 3d float.

What is the fastests / a fast way to perform to compute this computation?

Edit:

• The number of elements is appr. 256^3.
• 3d float means that the array can be accessed like this: array.GetValue(indexX, indexY, indexZ). I am not sure how the respective memory is organized in IronPython's System.Array.
• Background: I wrote an interface to an IronPython API, which gives access to data in a simulation software tool. I retrieve 3d scalar data and accumulate it to a temporal array in my IronPython script. The accumulation is performed 10,000 times and should be fast, so that the simulation does not take ages.

Willful

Could you provide more context? 3d float? A struct? What is the actual operation? 3 adds? How many elements is large? How often does the operation occur? – Evildoer

Extra context provided. – Willful

Answer 1

Is it possible to use the numpy library developed for IronPython?

https://pytools.codeplex.com/wikipage?title=NumPy%20and%20SciPy%20for%20.Net

It appears to be supported, and as far as I know is as close you can get in python to C style pointer functionality with arrays and such.

Create an array:

x = np.array([[1, 2, 3], [4, 5, 6]], np.int32)

Multiply all elements by 3.0:

x *= 3.0

Quincy

Unfortunately, as I am interfacing an API I cannot go away from System.Array. Converting to numpy first would not make it faster, really? – Willful

Can you try the generic access methods IE for i in range(0:ArrA.Count) : arrA[i] = arrA[i] + arrB[i]? Also compare with full on converting to numpy, then back to the array. Who knows? :) – Quincy

The generic access methods do not work on 3D arrays. As far as I know, for converting to numpy I have to do the same (time expensive loop) as shown in my question. – Willful

try x = np.array(ArrA) to initialize the array to numpy. Also I feel that the array you are using does not separate into tuples, so you have to use 3 gets. – Quincy