Mir vs. Numpy: Reworked!

[jmh530]

On Thursday, 3 December 2020 at 16:27:59 UTC, 9il wrote:
> Hi all,
>
> Since the first announcement [0] the original benchmark [1] has 
> been boosted [2] with Mir-like implementations.
>
> D+Mir:
>  1. is more abstract than NumPy
>  2. requires less code for multidimensional algorithms
>  3. doesn't require indexing
>  4. uses recursion across dimensions
>  5. a few times faster than NumPy for non-trivial real-world 
> applications.
>
> Why Mir is faster than NumPy?
>
> 1. Mir allows the compiler to generate specialized kernels 
> while NumPy constraints a user to write code that needs to 
> access memory twice or more times.
>
> Another Mir killer feature is the ability to write generalized 
> N-dimensional implementations, while Numpy code needs to have 
> separate implementations for 1D, 2D, and 3D cases. For example, 
> the main D loop in the benchmark can compile for 4D, 5D, and 
> higher dimensional optimizations.
>
> 2. @nogc iteration loop. @nogc helps when you need to control 
> what is going on with your memory allocations in the critical 
> code part.
>
> [0] 
> https://forum.dlang.org/post/pemharpztorlqkxdooul@forum.dlang.org
> [1] https://github.com/typohnebild/numpy-vs-mir
> [2] https://github.com/typohnebild/numpy-vs-mir/pull/1
>
> The benchmark [1] has been created by Christoph Alt and Tobias 
> Schmidt.
>
> Kind regards,
> Ilya

Looks good, but a few typos:

"The big difference is especially visible in this figures."

"For bigger prolem sizes the FLOP/s slightly drop and finally 
level out."

"Propably this is mainly caused by the overhead of the Python 
interpreter and might be reduced by more optimization efforts."