So, if I install numpy via conda on a Mac now, it's backed by Intel MKL and is thus amazingly fast. What will it be replaced with? Has anyone at Apple thought about use cases like this?..
I bet that they already have some low-level math library that uses ARM NEON intrinsics; you would definitely need them to port performance-demanding apps like Final Cut Pro / Maya / Photoshop / etc.
numpy is backed by BLAS and LAPACK. It just happens that on your system on macOS those libraries are provided my MKL. There are other implementations of BLAS and LAPACK out there.
In the Platforms State of the Union, they specifically listed Numpy as one of the open source projects that they built for Apple Silicon (along with Python 3 and others). Go to 20 minutes and 35 seconds on that video.
This isn't just about hardware. It's about software, and an investment in making libraries that are fully optimized for the hardware. The problem here is the long tail of needs is very, very long.
SciPy did support Accelerate, but they dropped it in 2018 because Apple’s implementation of LAPACK was so out of date. Apple have been crap at updating this stuff which is why people don’t use it.
I'm not a developer myself, but When I engaging with them on various OSS projects I always feel Windows is being treated as a second class citizen in almost every aspect (smaller projects often has no test, tool chain setup, tutorial, or all of above for Windows, as a starter).
