About alternatives to Matlab

[Jon Harrop]

sturlamolden wrote:
> I don't agree that slicing is not the best way to approach this
> problem.

Indeed, the C++ approach can be written very succinctly using slicing:

for i=0 to n/2-1 do
  tmp[i] = dot a[2*i:] h;
  tmp[i + n/2] = dot a[2*i + 1:] g;

where a[i:] denotes the array starting at index "i".

> In both NumPy and Matlab, slicing (often) results in calls to 
> BLAS/ATLAS, which are heavily optimized numerical routines. One can
> supply BLAS libraries that automatically exploits multiple CPUs, use
> FMA (fused multiply and add) whenever appropriate, and make sure cache
> is used in the most optimal way.

If the Python were making only a few calls to BLAS then I would agree.
However, in order to move as much computation as possible from the
interpreted language onto BLAS you have had to call BLAS many times.

So the super-fast BLAS routines are now iterating over the arrays many times
instead of once and the whole program is slower than a simple loop written
in C.

Indeed, this algorithm could be written concisely using pattern matching
over linked lists. I wonder if that would be as fast as using BLAS from
Python.

-- 
Dr Jon D Harrop, Flying Frog Consultancy
Objective CAML for Scientists
http://www.ffconsultancy.com/products/ocaml_for_scientists/index.html?usenet