Readily pin Julia threads to CPU processors
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Lightning talk (10 min) given at JuliaCon 2023 at MIT.
Dual-socket system where each CPU has 40 hardware threads (20 CPU-cores with 2-way SMT).
Check out the documentation to learn how to use ThreadPinning.jl.
The package is registered. Hence, you can simply use
] add ThreadPinning
to add the package to your Julia environment.
Note that ThreadPinning.jl, specifically the pinning functionality, only works on Linux. On other operating systems, all pinning calls (e.g. pinthreads) will be no-ops.
To gather information about the hardware topology of the system (e.g. sockets and memory domains), ThreadPinning.jl uses lscpu. The latter must therefore be available (i.e. be on PATH), which should automatically be the case on virtually all linux systems.
In the unlikely case that lscpu isn't already installed on your system, here are a few ways to get it
- install
util-linuxvia your system's package manager or manually from here - download the same as a Julia artifact: util_linux_jll.jl
By default, ThreadPinning.jl queries the system topology using lscpu on startup (i.e. at runtime). This is quite costly but is unfortunately necessary since you might have precompiled the package on one machine and use it from another (think e.g. login and compute nodes of a HPC cluster). However, you can tell ThreadPinning.jl to permanently skip this autoupdate at runtime and to always use the system topology that was present at compile time (i.e. when precompiling the package). This is perfectly safe if you don't use the same Julia depot on different machines, in particular if you're a "standard user" that uses Julia on a desktop computer or laptop, and can reduce the package load time significantly. To do so, simply call ThreadPinning.Prefs.set_autoupdate(false).
Because
- it effects performance (MFlops/s), in particular on HPC clusters with multiple NUMA domains
- it allows you to utilize performance counters inside of CPU-cores for hardware-performance monitoring
- it makes performance benchmarks more reliable (i.e. less random/noisy)
- ...
For more information, please find the documentation here.
CI infrastructure is provided by the Paderborn Center for Parallel Computing (PC²)