Re: 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.