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/[email protected]
[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."
