Using D and std.ndslice as a Numpy Replacement

[Jack Stouffer via Digitalmars-d-announce]

On Sunday, 3 January 2016 at 00:17:23 UTC, Ilya Yaroshenko wrote:
> On Sunday, 3 January 2016 at 00:09:33 UTC, Jack Stouffer wrote:
>> On Saturday, 2 January 2016 at 23:51:09 UTC, Ilya Yaroshenko 
>> wrote:
>>> This benchmark is _not_ lazy, so ndslice faster than Numpy 
>>> only 3.5 times.
>>
>> I don't know what you mean here, I made sure to call 
>> std.array.array to force allocation.
>
> In the article:
>     auto means = 100_000.iota <---- 100_000.iota is lazy range
>         .sliced(100, 1000)
>         .transposed
>         .map!(r => sum(r) / r.length)
>         .array;               <---- allocation of the result
>
> In GitHub:
>     means = data           <---- data is allocated array, it is 
> fair test for real world
>         .sliced(100, 1000)
>         .transposed
>         .map!(r => sum(r, 0L) / cast(double) r.length)
>         .array;             <---- allocation of the result
>  -- Ilya

I still have to disagree with you that the example I submitted 
was fair. Accessing global memory in D is going to be much slower 
than accessing stack memory, and sense most std.ndslice 
calculations are going to be on the stack, I believe my benchmark 
is indicative of normal use.