Hah, that looks exceptionally cool! 
/me needs to find time to play with it soon…
OvermindDL1
chrismcg
I definitely want to play with this now I just tried installing it on macOS 10.12.6 and am getting:
% mix compile
==> matrex
Compiling: native/src/matrix.c
In file included from native/src/matrix.c:1:
native/src/../include/matrix.h:4:10: fatal error: 'cblas.h' file not found
#include <cblas.h>
^~~~~~~~~
Is there something else I need to do to have it find the headers from the Accelerate framework?
0
chrismcg
So I had to add:
CFLAGS += -I/System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Versions/Current/Headers/
To the ifeq (BLAS, blas) section of the Makefile in deps/matrex to get it to compile.
2
OvermindDL1
Or could just use cmake and hunter or so to always acquire and use clblas or openblas or whatever as well?
0
versilov
Thanks for the bug report. I’ve added include path to the Makefile.
Please, check if it works on your systems.
1
chrismcg
versilov
Compared Matrex performance to NumPy. Looks like we are going more or less on par, with Matrex being two times faster on element-wise operations and a bit slower on dot product.
2015 MacBook Pro, 2.2 GHz Core i7, 16 GB RAM
Operations are performed on 3000×3000 matrices filled with random numbers.
You can run benchmarks from the /bench folder with python numpy_bench.py and MIX_ENV=bench mix bench commands.
NumPy
benchmark iterations average time
np.divide(A, B) 30 15.43 ms/op
np.add(A, B) 100 14.62 ms/op
sigmoid(A) 50 93.28 ms/op
np.dot(A, B) 10 196.57 ms/op
Matrex
benchmark iterations average time
divide(A, B) 200 7.32 ms/op (~ 2× faster)
add(A, B) 200 7.71 ms/op (~ 2× faster)
sigmoid(A) 20 71.47 ms/op (23% faster)
dot(A, B) 10 213.31 ms/op (8% slower)
15
sym_num
sym_num
Mr. Versilov, your Matrex is very helpful. Thank you so much. Thanks to you, I can easily use CBLAS from Elixir.
By the way, I have a request. Could you make a library that uses CuBLAS（NVIDIA) from Elixir? Or please tell me how to use CuBLAS from Elixir.
Kenichi Sasagawa
2
versilov
Than you for the praise!
BLAS is generally just an API standard, so using CuBLAS should be a matter of compiling Matrex with the CuBLAS library linked.
Do I need an NVIDIA accelerator to test CuBLAS or not?
1