Thanks! That's an excellent point. You're right that there's likely a sweet spot that would be more efficient overall than aggressive throttling.
The current implementation uniformly sets max frequency for all 128 cores, but I'm working on per-core frequency control that would allow much more granular optimization. I'll definitely measure aggregate consumption with your suggestion versus my current implementation to see the difference.
Please go learn about modern Ryzen power and performance management, namely Precision Boost Overdrive and Curve Optimizer - and how to undervolt an AM4/AM5 processor.
The stuff the chip and motherboard do, completely built-in, is light-years ahead of what you're doing. Your power-saving techniques (capping max frequency) are more than a decade out of date.
You'll get better performance and power savings to boot.
Thanks for the suggestion! I'm actually using dual EPYC server processors in this workstation, not Ryzen. I'm not sure EPYC supports PBO/Curve Optimizer functionality that's available in AM4/AM5 platforms.
That said, I'm definitely interested in learning more about processor-specific optimizations for EPYC. If there are server-focused equivalents to what you've mentioned that would work better than frequency capping, I'd love to explore them!
For people with Intel processors, check out raplcap: https://github.com/powercap/raplcap
It lets you set specific power consumption limits in W instead of attempting to do the same by restricting maximum core frequencies (which could also be useful in addition to overall power limits).
Another suggestion: when you want to save power, use irq affinity with /proc/irq/$irq/smp_affinity_list to put them all on one core.
This core will get to sleep less than the others.
You can also use the CPU "geometry" (which cores share cache) to set max frequency on its neighboring cores first, before recruiting the other cores
Zooming out, 80-90% of a computer's lifecycle energy use is during manufacturing, not pulled from the wall during operation.[1] To optimize lifetime energy efficiency, it probably pushes toward extending hardware longevity (within reason, until breakeven) and maximizing compute utilization.
Ideally these goal are balanced (in some 'efficient' way) against matching electricity prices. It's not either/or, you want to do both.
Besides better amortizing the embodied energy, improving compute utilization could also mean increasing the quality of the compute workloads, ie doing tasks with high external benefits.
Love this project! Thanks for sharing.
[1] https://forums.anandtech.com/threads/embodied-energy-in-comp...