Standard D, Mir D benchmarks against Numpy (BLAS)

[Pavel Shkadzko]

I have done several benchmarks against Numpy for various 2D 
matrix operations. The purpose was mere curiosity and spread the 
word about Mir D library among the office data engineers.
Since I am not a D expert, I would be happy if someone could take 
a second look and double check.

https://github.com/tastyminerals/mir_benchmarks

Compile and run the project via: dub run --compiler=ldc 
--build=release

*Table descriptions reduced to fit into post width.

+---------------------------------+---------------------+--------------------+---------------------+
| Description                     | Numpy (BLAS) (sec.) | 
Standard D (sec.)  | Mir D (sec.)        |
+---------------------------------+---------------------+--------------------+---------------------+
| sum of two 250x200 (50 loops)   | 0.00115             | 
0.00400213(x3.5)   | 0.00014372(x1/8)    |
| mult of two 250x200 (50 loops)  | 0.0011578           | 
0.0132323(x11.4)   | 0.00013852(x1/8.3)  |
| sum of two 500x600 (50 loops)   | 0.0101275           | 
0.016496(x1.6)     | 0.00021556(x1/47)   |
| mult of two 500x600 (50 loops)  | 0.010182            | 
0.06857(x6.7)      | 0.00021717(x1/47)   |
| sum of two 1k x 1k (50 loops)   | 0.0493201           | 
0.0614544(x1.3)    | 0.000422135(x1/117) |
| mult of two 1k x 1k (50 loops)  | 0.0493693           | 
0.233827(x4.7)     | 0.000453535(x1/109) |
| Scalar product of two 30k       | 0.0152186           | 
0.0227465(x1.5)    | 0.0198812(x1.3)     |
| Dot product of 5k x 6k, 6k x 5k | 1.6084685           | 
--------------     | 2.03398(x1.2)       |
| L2 norm of 5k x 6k              | 0.0072423           | 
0.0160546(x2.2)    | 0.0110136(x1.6)     |
| Quicksort of 5k x 6k            | 2.6516816           | 
0.178071(x1/14.8)  | 1.52406(x1/0.6)     |
+---------------------------------+---------------------+--------------------+---------------------+