[numpy @ bigsur: multithreading]

[Maxim Abalenkov]

Dear all,

I’m looking for guidance please. I would like to make sure, that I use all eight of my CPU cores, when I run Python’s 3.9.9 NumPy on my macOS BigSur 12.1. When I run my NumPy code, I see in ‘htop’, that only one ‘python’ process is running and the core utilisation is 20–25%. I remember in the past, stock MacPorts NumPy installation would use Apple’s Accelerate library including the multithreaded BLAS and LAPACK (https://developer.apple.com/documentation/accelerate). As I understand this is no longer the case.

I run Python code using a virtual environment located under

 /opt/venv/zipfstime/lib/python3.9/site-packages/numpy/core

When I change there and issue

 otool -L _multiarray_umath.cpython-39-darwin.so

_multiarray_umath.cpython-39-darwin.so:
	@loader_path/../.dylibs/libopenblas.0.dylib (compatibility version 0.0.0, current version 0.0.0)
	/usr/lib/libSystem.B.dylib (compatibility version 1.0.0, current version 1281.100.1)

In other words, NumPy relies on openBLAS. Command `port variants openblas` returns

OpenBLAS has the variants:
  g95: Build using the g95 Fortran compiler
    * conflicts with gcc10 gcc11 gcc8 gcc9 gccdevel
  gcc10: Build using the MacPorts gcc 10 compiler
    * conflicts with g95 g95 gcc11 gcc8 gcc9 gccdevel
[+]gcc11: Build using the MacPorts gcc 11 compiler
    * conflicts with g95 g95 gcc10 gcc8 gcc9 gccdevel
  gcc8: Build using the MacPorts gcc 8 compiler
    * conflicts with g95 g95 gcc10 gcc11 gcc9 gccdevel
  gcc9: Build using the MacPorts gcc 9 compiler
    * conflicts with g95 g95 gcc10 gcc11 gcc8 gccdevel
  gccdevel: Build using the MacPorts gcc devel compiler
    * conflicts with g95 g95 gcc10 gcc11 gcc8 gcc9
[+]lapack: Add Lapack/CLapack support to the library
  native: Force compilation on machine to get fully optimized library
  universal: Build for multiple architectures

I tried installing the “native” variant of OpenBLAS port with `sudo port install openblas +native` and setting the environment variable `OMP_NUM_THREADS=8`, but I didn’t see any improvement when running my Python code. I would welcome your help and guidance on this subject.

—
Best wishes,
Maxim