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.
