JuliaLang / julia / #36985

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<a href="https://github.com/JuliaLang/julia/commit/<a class=hub.com/JuliaLang/julia/commit/d0d895e92e22eb259a69b2b591b0cba41c1450e1">d0d895e92<a href="https://github.com/JuliaLang/julia/commit/d0d895e92e22eb259a69b2b591b0cba41c1450e1">&quot;&gt;jl_cpu_threads: exclude big.LITTLE efficency cores (#42099)

* jl_cpu_threads: exclude big.LITTLE efficency cores

On big.LITTLE systems, we generally only want to spawn as many
threads/tasks as there are performance cores.  By default, we want to
leave the efficiency cores alone, as they may end up choking on the
heavy workloads we are likely to schedule.  Even something as simple as
starting `julia` and initializing OpenBLAS on each thread can cause a
system-wide latency spike as the efficiency cores struggle to chew
through the momentary workload.

To fix this, we attempt to identify when we are running on a big.LITTLE
system (the only one currently widely supported is the Apple M1), and
we subtract out the known number of efficiency cores.  Once macOS 12 is
released, we will be able to use the official API for enumerating the
perflevels of the available cores, demonstrated in this PR to pytorch&amp;#39;s
cpuinfo repository [0].

[0] https://github.com/pytorch/cpuinfo/blob/</a><a class="double-link" href="https://github.com/JuliaLang/julia/commit/<a class="double-link" href="https://github.com/JuliaLang/julia/commit/8ab2db2d405436f1014ed603021545b3b6b6f1ae">8ab2db2d4</a>">8ab2db2d4</a><a href="https://github.com/JuliaLang/julia/commit/d0d895e92e22eb259a69b2b591b0cba41c1450e1">/src/arm/mach/init.c#L161-L163

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