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Name Unit Default value (default;min;max) Description Example
units None 1;1;1 CPU quantity 2
usage None See Usage See usage ..
core_units None 24;1;64 Number of physical core on one CPU 12
die_size mm2 None Size of the die 1.1
embedded None None Name of the CPU embedded AMD
die_size_per_core mm2 None Size of the die divided by the number of core 0.245
model_range None None Name of the cpu range or brand i7
family None None Name of the architectural family (Generation) Skylake
name None None Complete commercial name of the CPU Intel Core i7-1065
tdp Watt None Thermal Design Point 250


The following completion strategies can be used

Completion from CPU name

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


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 the config file to control the behavior of the fuzzy matching. You can contribute to the cpu name repository by following the instructions in the documentation.

Completion of the die_size from family and/or core_units

if die_size_per_core and core_units are given :

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

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

If several cpu matches the given family, we use the core_units attributes :

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

Embedded impacts

Impacts criteria

Criteria Implemented Source
gwp yes Green Cloud Computing, 2021
adp yes Green Cloud Computing, 2021
pe yes Green Cloud Computing, 2021
gwppb no
gwppf no
gwpplu no
ir no
lu no
odp no
pm no
pocp no
wu no
mips no
adpe no
adpf no
ap no
ctue no
ctuh_c no
ctuh_nc no
epf no
epm no
ept no

Impact factors

For one CPU the embedded impact is:

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


Constant Units Value
\(\text{CPU}_\text{embedded_die}^{\text{gwp}}\) kgCO2eq/mm2 1.97e−02
\(\text{CPU}_\text{embedded_die}^{\text{adp}}\) kgSbeq/mm2 5.87e-09
\(\text{CPU}_\text{embedded_die}^{\text{pe}}\) MJ/mm2 0.265
\(\text{CPU}_\text{embedded_base}^{\text{gwp}}\) kgCO2eq 9.14
\(\text{CPU}_\text{embedded_base}^{\text{adp}}\) kgSbeq 2.04E-02
\(\text{CPU}_\text{embedded_base}^{\text{pe}}\) MJ 156.00


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

Usage impacts

Both power consumption and consumption profile are implemented.

Power Consumption profile

The CPU consumption profile is of the form:

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


We apply a log regression to fit data points \((workload, power)\) starting from a default consumption profile that can be found using the CPU model_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

If model_range is given or is completed from the cpu_name, we use the averaged parameter for the specific model range.

manufacturer model_range a b c d
Intel Xeon Platinum 171.1813 0.0354 36.8953 -10.1336
Intel Xeon Gold 35.5688 0.2438 9.6694 -0.6087
Intel Xeon Silver 20.7794 0.3043 8.4241 0.8613
Intel Xeon E5 48.9167 0.1349 15.7262 -4.654
Intel Xeon E3 342.3624 0.0347 36.8952 -16.4022
Intel Xeon E 55.6501 0.0467 20.4146 4.24362

By default, we use the consumption profile of Intel Xeon Platinum


Model adaptation from punctual measurement

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


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%
0.12 0.32 0.75 1.02
Average power consumption per unit of TDP