• Turning ON deep c-states. This might increase startup-latency of some workloads, but since Cores can not only go into one fixed Turbo Boost frequency, but actually if only SOME cores go into a stronger Turbo Boost, then they can reach even higher frequencies! So if you have a workload that is single or low-threaded you can profit from extraordinary high frequencies that you might never see when all cores are always running on higher frequencies.
  • Source: https://www.vmware.com/explore/video-library/video-landing.html?sessionid=1686331461690001FeTn&videoId=6340661293112

Turbo Boost is the branded name from Intel for a technology called Dynamic Frequency and Voltage Scaling (DVFS).

DVFS is available in every modern Intel or AMD CPU. the name in AMD CPUs is Turbo Core

Turbo Boost can be checked through the linux subsystem, but also by querying the CPU registers directly.

You can find a script to check if Turbo Boost is on or off on your system in our Github Tools repository.

Turbo Boost as a feature enables the processor to overshoot it’s base frequency for a certain amount of time. This enables snappy responsiveness when instantaneous load happens.

The downside of the feature is that it uses often exponentially more energy and can be detremential to energy cost of the system as a whole.

The question that arises for someone who is doing research in software energy consumption is:

• How much energy / power does Turbo Boost / SMT need to provide it’s functionality?
• What are the drawbacks of Turbo Boost?
• What speed achievements can I achieve?
• Should I turn Turbo Boost off when when energy is my primary concern?
• Does it help “running” to the completion of my calculation and then turn off? (Race to sleep)

Energy test

Our test machines are:

• MacBook Pro 13" 2015 model with a Intel Core i7-5557U CPU @ 3.1 GHz.
• Quanta Leopard-DDR3
• Fujitsu TX1330 M3

Find details about the machines in our cluster specification

According to /proc/cpuinfo the MacBook Pro 13" 2015 for instance has a chip with 2 physical cores (found by looking at max. core id number) and 4 threads (found by looking at max. processor number).

Looking at flags we see that ht is a feature, which corresponds to Hyper-Threading.

In order to have a first glimpse at the energy characterisitcs of this feature we are using sysbench, which you can just install through aptitude on Ubuntu 22.04.

The command we ran in sysbench is:

./turbo_boost.sh disable
sleep 180
perf stat -a -e power/energy-pkg/ sysbench --cpu-max-prime=10000 --threads=48 --test=cpu --events=300000 --time=0 run
./turbo_boost.sh enable
sleep 180
perf stat -a -e power/energy-pkg/ sysbench --cpu-max-prime=10000 --threads=48 --test=cpu --events=300000 --time=0 run

The command always calculates 300,000 Events fixed with no time limit.

This is the result:

Result: We see that the decrease in time sometimes does not outweigh the increase in energy. Especially for the MacBook machine this is very prononounced.

Real world case

An important question is however also: How does this compare in a real-world use-case? Will results be the same?

We picked the Unit-Tests of the Django project (https://github.com/green-coding-solutions/example-applications/tree/main/django_tests) and run the tests with Turbo-boost off and on.

Important: If you have Turbo-Boost off and you have high loads on Hard-Disks the cut-off point when it makes sense to turn on / off Turbo Boost will occur earlier.

In a Django Unit-Test run with Turbo Boost On we see that the CPU-Package power is at 21.3 W and equates over the runtime of 171 s to an energy budget of 3645.30 J.

For the Django Unit-Test run with Turbo Boost Off we see that the CPU-Package power is at 12.87 W and equates over the runtime of 218 s to an energy budget of 2805.90 J.

So even in this real-world scenarios we see here that Turbo-Boost is a detremential feature when looking at the CPU Energy cost.

Discussion

However, this is not the whole story.

When looking at energy costs you always have to expand the picture as much as you can:

• How much auxilliary devices are running that also consume power?
  • HDDs
  • SDDs
  • RAM
  • etc.

We see that the the CPU Package power dropped from 21.3 W -> 12.87 W (39.5 % reduction), but looking at the whole machine (psu_power_ac_powerspy2_system) we see that the drop is not that significant 42.17 W -> 32.62 W (22.64 % reduction). This makes perfect sense, as the CPU is only a part of the whole machine and the other components still run and are not affected by Turbo Boost.

We see in this particular case, that the gain is still beneficial. The total energy (psu_energy_ac_powerspy2_system) for the Turbo Boost Off case is still lower (7113.10 J vs. 7215.67 J)

If you are however in a datacenter, where the system is maybe externally cooled AND the cooling could be turned off if the machine can also be turned off, then you would have a case where it would make sense to have Turbo Boost turned on for this particular hardware setup.

So you always have to make the scope as wide as you reasonably can and include all parts you use energy for.

What is the conclusion now?

If you can somehow quantify the time that you are loosing in energy, than make a trade-off calculation.

If you typically have long idle or even shutdown periods in between and there is no good reason why the calculation should be faster (which is often the case for many tasks in a system) -> off

Did we miss something? Please shoot us an email to [email protected]