Coping with 64-bit code, APFS, the different CPU, the SSV, System Settings, Recovery Mode, and how to get the best from migration and sharing in iCloud.
Apple silicon
A strange observation, that the last thread to complete a matrix multiplication task was always much later than others, explored to discover a different strategy used by macOS.
Running a macOS VM on Apple silicon has many advantages: it lets you run older macOS on newer models, is more secure, and convenient, except it can’t work with App Store apps.
If you use macOS VMs on an Apple silicon Mac, folders shared with the host may vanish in 14.2 and later. Here’s why, which are affected, and how to work around the problem.
Comparison between 2 Intel and 2 Apple silicon Macs running vector and matrix functions from Apple’s Accelerate library. Was that new M3 worth the money?
There’s more to getting best performance and energy efficiency on Apple silicon. These vary greatly depending on how apps are coded, as shown here.
If Apple offered to do much of the hard work of coding your app for you for free, and to optimise it for different Mac hardware, how could you refuse?
How to compare an undocumented if not secret co-processor? Using different tests that use very high power, and can result in strange patterns of core allocation. So how does the M3 Pro fare here?
Comparison with M1 variants, energy use with comparison between M3 Pro and Max, virtualisation, Game Mode, vector processing and matrix co-processing – all in summary.
Assessing throughput using tests of fast Fourier transforms and sparse Cholesky factorisation from the Accelerate library. Is there an AMX there?
The Eclectic Light Company 