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CPU

Characteristics

NameUnitDefault value (default;min;max)DescriptionExample
unitsNone1;1;1CPU quantity2
usageNoneSee UsageSee usage..
core_unitsNone24;1;64Number of physical core on one CPU12
die_sizemm2NoneSize of the die1.1
embeddedNoneNoneName of the CPU embeddedAMD
die_size_per_coremm2NoneSize of the die divided by the number of core0.245
model_rangeNoneNoneName of the cpu range or brandi7
familyNoneNoneName of the architectural family (Generation)Skylake
nameNoneNoneComplete commercial name of the CPUIntel Core i7-1065
tdpWattNoneThermal Design Point250

Complete

The followingcompletion strategies can be used

Completion from CPUname

If CPUname is given,model_range,tdp,die_size andfamily can be retrieved from a fuzzy matching on our cpu name repository.

Warning

Note that the cpu name repository is not complete and the completion can return a different cpu than the one given by the user. You can set a threshold for the fuzzy matching in theconfig file to control the behavior of the fuzzy matching. You can contribute to the cpu name repository by following the instructions in thedocumentation.

Completion of thedie_size fromfamily and/orcore_units

ifdie_size_per_core andcore_units are given :

\[ \text{die_size} = {\text{core_units}}*{\text{die_size_per_core}}\]

Otherwise, iffamily is given,die_size can be retrieved from a fuzzy matching on our cpu repository.

If several cpu matches the givenfamily, we use thecore_units attributes :

  • Ifcore_units matches one to many cpu, the average value is given and min and max value are used asmin andmax fields.
  • Ifcore_units does not match any cpu, we infer thedie_size with a rule of three or a linear regression (when multiple cpus are available).
  • Ifcore_units is not provided, the average value is given and min and max value are used asmin andmax fields.

Embedded impacts

Impacts criteria

CriteriaImplementedSource
gwpyesGreen Cloud Computing, 2021
adpyesGreen Cloud Computing, 2021
peyesGreen Cloud Computing, 2021
gwppbno
gwppfno
gwppluno
irno
luno
odpno
pmno
pocpno
wuno
mipsno
adpeno
adpfno
apno
ctueno
ctuh_cno
ctuh_ncno
epfno
epmno
eptno

Impact factors

For one CPU the embedded impact is:

\[ \text{CPU}_\text{embedded}^\text{criteria} = (\text{CPU}_{\text{die_size}}) * \text{CPU}_\text{embedded_die}^\text{criteria} + \text{CPU}_\text{embedded_base}^\text{criteria}\]

with:

ConstantUnitsValue
\(\text{CPU}_\text{embedded_die}^{\text{gwp}}\)kgCO2eq/mm21.97e−02
\(\text{CPU}_\text{embedded_die}^{\text{adp}}\)kgSbeq/mm25.87e-09
\(\text{CPU}_\text{embedded_die}^{\text{pe}}\)MJ/mm20.265
\(\text{CPU}_\text{embedded_base}^{\text{gwp}}\)kgCO2eq9.14
\(\text{CPU}_\text{embedded_base}^{\text{adp}}\)kgSbeq2.04E-02
\(\text{CPU}_\text{embedded_base}^{\text{pe}}\)MJ156.00

Info

If there are more than 1 CPU we multiply\(\text{CPU}_\text{embedded}^\text{criteria}\) by the number of CPU given inunits.

Usage impacts

Bothpower consumption andconsumption profile are implemented.

Power Consumption profile

The CPU consumption profile is of the form:

\[PowerConsumption(workload) = a * \ln(b * (workload + c)) + d\]

Warning

We apply a log regression to fit data points\((workload, power)\) starting from a default consumption profile that can be found using the CPUmodel_range. This process can (in some cases) yield very low or negative power values due to wrong input data or model initialization.That is why there is a minimum power consumption limit set to 1W for any input workload.

Determining the parameters

From model range

Ifmodel_range is given or is completed from thecpu_name, we use the averaged parameter for the specific model range.

manufacturermodel_rangeabcd
IntelXeon Platinum171.18130.035436.8953-10.1336
IntelXeon Gold35.56880.24389.6694-0.6087
IntelXeon Silver20.77940.30438.42410.8613
IntelXeon E548.91670.134915.7262-4.654
IntelXeon E3342.36240.034736.8952-16.4022
IntelXeon E55.65010.046720.41464.24362

By default, we use the consumption profile ofIntel Xeon Platinum

cp_cpu_xeon_platinum.svg

Model adaptation from punctual measurement

In case punctual power measurement (load;power_consumption) are given by a user, we adapt the selected consumptionprofile to match the given point.

cp_cpu_fine_tuned.svg

Model adaptation from TDP

If the TDP is given we use the average power consumption per unit of TDP (given by TEADS) multiplied by the given TDP as power measurement and compute a model adaptation as describe above.

0%10%50%100%
0.120.320.751.02
Average power consumption per unit of TDP

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