Unlocking Python's Cores:Energy Implications of Removing the GIL
1–10 of 111 posts
Re: Unlocking Python's Cores:Energy Implications of Removing the GIL
#2Title shortened - Original title:
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.
Re: Unlocking Python's Cores:Energy Implications of Removing the GIL
#3Might be worth noting that this seems to be just running some tests using the current implementation, and these are not necessarily general implications of removing the GIL.
Re: Unlocking Python's Cores:Energy Implications of Removing the GIL
#4One thing I'm curious about here is the operational impact.
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.
Re: Unlocking Python's Cores:Energy Implications of Removing the GIL
#5Might be worth noting that this seems to be just running some tests using the current implementation, and these are not necessarily general implications of removing the GIL.
There might also be many optimization opportunities that still have to be seized.
Re: Unlocking Python's Cores:Energy Implications of Removing the GIL
#6Sections 5.4 and 5.5 are the interesting ones.
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.
Re: Unlocking Python's Cores:Energy Implications of Removing the GIL
#7[flagged]
Re: Unlocking Python's Cores:Energy Implications of Removing the GIL
#8Sections 5.4 and 5.5 are the interesting ones. 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.
I'm not sure of the exact relationship, but power consumption increases greater than linear with clock speed. If you have 4 cores running at the same time, there's more likely to be thermal throttling → lower clock speeds → lower energy consumption.
Greater power draw though; remember that energy is the integral of power over time.
Re: Unlocking Python's Cores:Energy Implications of Removing the GIL
#9Sections 5.4 and 5.5 are the interesting ones. 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.
I'm not sure of the exact relationship, but power consumption increases greater than linear with clock speed. If you have 4 cores running at the same time, there's more likely to be thermal throttling → lower clock speeds → lower energy consumption. Greater power draw though; remember that energy is the integral of power over time.
By running more tasks in parallel across different cores they can each run at lower clock speed and potentially still finish before a single core at higher clock speeds can execute them sequentially.
Re: Unlocking Python's Cores:Energy Implications of Removing the GIL
#10[flagged]
The obvious solution is to require libraries that are no-GIL safe to declare that, and for all other libraries implicitly wrap them with GIL locks.