Obviously, you should vectorize your code and avoid explicit loops. In both Octave and NumPy
However, what the thread is pointing out is that if you do have explicit for loops, Octave is much slower than NumPy.
For the code I was writing, I had trouble vectorizing a particular piece of code. I'm sure if I spent many extra hours, I'd figure out some way. But why should I when it's 7x faster in NumPy without vectorizing?
And whatever way I would discover for vectorizing it in Octave would likely work for NumPy as well. It'll be a rare exception that NumPy is slower than Octave.
For me, the only reason to use Octave is if someone hands me Matlab legacy code. For my scientific computation, NumPy/SciPy has always been superior.
Ironically, Octave usage is inherently tied to Matlab's existence. Were Matlab to disappear, Octave would have little use when it comes to developing new code. I cannot think of a good reason for someone new to the field to develop in Octave, other than the need to interface with others who use Matlab.
I know this comment sounds critical, but I'm not anti-Octave. When I first started using it, I was very impressed, and I learned far more about Matlab by using Octave and reading Octave's docs than any docs from Mathworks. As a Matlab clone, it is superb.
It's just that now the alternatives to Octave are superior. Personally, I would like to see development continue for Octave, just because I appreciate it so much as an open source project. But the reality is when I give new young scientists advice, it is almost always "Use NumPy/SciPy".
Obviously, you should vectorize your code and avoid explicit loops. In both Octave and NumPy
However, what the thread is pointing out is that if you do have explicit for loops, Octave is much slower than NumPy.
For the code I was writing, I had trouble vectorizing a particular piece of code. I'm sure if I spent many extra hours, I'd figure out some way. But why should I when it's 7x faster in NumPy without vectorizing?
And whatever way I would discover for vectorizing it in Octave would likely work for NumPy as well. It'll be a rare exception that NumPy is slower than Octave.
For me, the only reason to use Octave is if someone hands me Matlab legacy code. For my scientific computation, NumPy/SciPy has always been superior.
Ironically, Octave usage is inherently tied to Matlab's existence. Were Matlab to disappear, Octave would have little use when it comes to developing new code. I cannot think of a good reason for someone new to the field to develop in Octave, other than the need to interface with others who use Matlab.
I know this comment sounds critical, but I'm not anti-Octave. When I first started using it, I was very impressed, and I learned far more about Matlab by using Octave and reading Octave's docs than any docs from Mathworks. As a Matlab clone, it is superb.
It's just that now the alternatives to Octave are superior. Personally, I would like to see development continue for Octave, just because I appreciate it so much as an open source project. But the reality is when I give new young scientists advice, it is almost always "Use NumPy/SciPy".