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
Hi Ilya,
Thanks a lot for sharing the update. I am currently working on
porting a python package called FMPY to D. This package makes
usage of numpy and I hope I can use MIR here.
Somehow it is hard to get started to learn MIR. What maybe could
help python developers is to have some articles showing numpy
coding and side by side the equivalent MIR coding.
What I miss in MIR is a function to read and write CSV files. Is
s.th. like numpy.genfromtxt planned?
Kind regards
Andre
