[Jprogramming] J v NumPy (was Re: J v Python)

[Konrad Hinsen]

Raul Miller writes:

 > Even then your dominant cost is reading the data off of disk.  If the
 > data is converted on the way in from the disk to RAM, and most of your
 > CPU time is spent waiting for the disk, your conversion time might not
 > even be measurable.

My situation would be that the data is read to memory, ending up in a
NumPy array, and then I have to copy it to a J array in a subsequent
step. That's why I am interested in exploring the shared data approach.

 > Note that if your data structure occupies half of your physical
 > memory, J intermediate results might be expensive -- my rule of thumb
 > (just a guess based on raw data size, until I get real measurements)
 > is to expect 5x memory overhead from J.

That's also a problem with NumPy, but there I know how to get around
it.  With J, that remains to be explored.

 > That said,  have you seen the pages linked from
 > http://www.jsoftware.com/help/user/dlls.htm?

Seen, yes, read in detail, no.

Thanks for the suggestions,
  Konrad.