D for project in computational chemistry

[John Colvin via Digitalmars-d]

On Sunday, 16 August 2015 at 13:59:33 UTC, Idan Arye wrote:
> Initially I thought the Python version is so slow because it 
> uses `range` instead of `xrange`, but I tried them both and 
> they both take about the same, so I guess the Python JIT(or 
> even interpreter!) can optimize these allocations away.
>
> BTW - if you want to iterate over a range of numbers in D, you 
> can use a foreach loop:
>
>     foreach (i; 0 .. l) {
>         foreach (j; 0 .. l) {
>             foreach (m; 0 .. l) {
>                 a = a + i * i * 0.7 + j * j * 0.8 + m * m * 0.9;
>             }
>
>         }
>     }
>
> Or, to make it look more like the Python version, you can 
> iterate over a range-returning function:
>
>     import std.range : iota;
>     foreach (i; iota(l)) {
>         foreach (j; iota(l)) {
>             foreach (m; iota(l)) {
>                 a = a + i * i * 0.7 + j * j * 0.8 + m * m * 0.9;
>             }
>
>         }
>     }
>
> There are also functions for building ranges from other ranges:
>
>     import std.algorithm : cartesianProduct;
>     import std.range : iota;
>     foreach (i, j, m; cartesianProduct(iota(l), iota(l), 
> iota(l))) {
>         a = a + i * i * 0.7 + j * j * 0.8 + m * m * 0.9;
>     }
>
> Keep in mind though that using these functions, while making 
> the code more readable(to those with some experience in D, at 
> least), is bad for performance - for my first version I got 
> about 5 seconds when building with DMD in debug mode, while for 
> the last version I get 13 seconds when building with LDC in 
> release mode.

There is a new implementation of cartesianProduct that makes the 
performance difference disappear for me with ldc and dmd. It's 
not in ldc's phobos yet so I had to copy it manually, but 
hopefully it will be in the next release.