[Marazan]

Simply replacing your own vector_3d class with numpy.array() won't actually speed you up that much as the overhead of creating all the tiny 3 element arrays kills you (I think I got only a 2x speed up from going from a pure python vector_3d to numpy arrays). Numpy is optimised for the creation of a small number of big arrays.

The massive enormous speed ups come from creating massive arrays and implicitly working on them in parallel. So instead of iterating through every pixel and creating a origin and direction vector for each one you create a Total_number_of_pixels X 3 numpy array and pass that to your ray tracing function. Due to the way numpy broadcasts arrays the amount of rewriting you need to do is incredibly minimal and the speed ups over pure python are astronomical.

[signa11]

> The massive enormous speed ups come from creating massive arrays and implicitly working on them in parallel. So instead of iterating through every pixel and creating a origin and direction vector for each one you create a Total_number_of_pixels X 3 numpy array and pass that to your ray tracing function. Due to the way numpy broadcasts arrays the amount of rewriting you need to do is incredibly minimal and the speed ups over pure python are astronomical.

thank you for your insight! this is very useful indeed.

[Marazan]

Just to give an idea of the speed up from going from processing individual arrays to batch processing - an Image I was rendering went from 2 hours to 20 seconds (and that was with further obvious speed ups left on the table as well).

Oh, and make sure you're using 64-bit version of python as you'll be slinging multi-gigabyte arrays around all over the place.