In production systems we often see Python services scaling horizontally because of the GIL limitations. If true parallelism becomes common, it might actually reduce the number of containers/services needed for some workloads.
But that also changes failure patterns — concurrency bugs, race conditions, and deadlocks might become more common in systems that were previously "protected" by the GIL.
It will be interesting to see whether observability and incident tooling evolves alongside this shift.
And for smaller projects it's such an annoyance. Having a simple project running, and having to muck around to get cron jobs, background/async tasks etc. to work in a nice way is one of the reasons I never reach for python in these instances. I hope removing the GIL makes it better, but also afraid it will expose a whole can of worms where lots of apps, tools and frameworks aren't written with this possibility in mind.
5.4: Energy consumption going down because of parallelism over multiple cores seems odd. What were those cores doing before? Better utilization causing some spinlocks to be used less or something?
5.5: Fine-grained lock contention significantly hurts energy consumption.
Greater power draw though; remember that energy is the integral of power over time.
Previously we had to use ProcessPoolExecutor which meant maintaining multiple copies of the runtime and shared data in memory and paying high IPC costs, being able to switch to ThreadPoolExecutor was hugely beneficially in terms of speed and memory.
It almost feels like programming in a modern (circa 1996) environment like Java.
Measure aggressively and test under real concurrency: use tracemalloc to find memory hotspots, py-spy or perf to profile contention, and fuzz C extension paths with stress tests so bugs surface in the lab not in production. Watch per thread stack overhead and GC behavior, design shared state as immutable or sharded, keep critical sections tiny, and if process level isolation is still required stick with ProcessPoolExecutor or expose large datasets via read only mmap.
runningmike•3d ago
Unlocking Python’s Cores: Hardware Usage and Energy Implications of Removing the GIL
I am curious about the NumPy workload choice made, due to more limited impact on CPython performance.