Python CPU

Terry Reedy tjreedy at
Mon Apr 4 19:14:40 CEST 2011

On 4/4/2011 5:23 AM, Paul Rubin wrote:
> Gregory Ewing<greg.ewing at>  writes:
>> What might help more is having bytecodes that operate on
>> arrays of unboxed types -- numpy acceleration in hardware.
> That is an interesting idea as an array or functools module patch.
> Basically a way to map or fold arbitrary functions over arrays, with a
> few obvious optimizations to avoid refcount churning.  It could have
> helped with a number of things I've done over the years.

For map, I presume you are thinking of an in system code 
(C for CPython) equivalent to

def map(self,func):
   for i,ob in enumerate(self):
     self[i] = func(ob)

The question is whether it would be enough faster. Of course, what would 
really be needed for speed are wrapped system-coded funcs that map would 
recognize and pass and received unboxed array units to and from. At that 
point, we just about invented 1-D numpy ;-).

I have always thought the array was underutilized, but I see now that it 
only offers Python code space saving at a cost of interconversion time. 
To be really useful, arrays of unboxed data, like strings and bytes, 
need system-coded functions that directly operate on the unboxed data, 
like strings and bytes have. Array comes with a few, but very few, 
generic sequence methods, like .count(x) (a special-case of reduction).

Terry Jan Reedy

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