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@sustainable-computing-io/reviewer |
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@sunya-ch can you add note to the symbols |
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For the sake of clarity, what about instead of we say
Some methods will not report some of these metrics, by listing that we can make it much more transparent Additionally, we can define Finally, we might have:
I am planning to refactor the metrics and the code to reflect this logic of |
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Here is the power model application flow logic I can think of right now. flowchart TD;
ACPI--installed-->NodeTotalPower=ACPI;
NodeTotalPower=ACPI-->RAPL;
ACPI--not installed-->NodeTotalPowerModel--available-->NodeTotalPower=NodeTotalPowerModel;
NodeTotalPowerModel--not available-->NodeTotalPower=NodeComponentPowers;
NodeTotalPower=NodeTotalPowerModel-->RAPL;
NodeTotalPower=NodeComponentPowers-->RAPL;
RAPL--support-->NodeComponentPowers=RAPL;
RAPL--not support--> NodeComponentPowerModel;
NodeComponentPowerModel--available-->NodeComponentPowers=NodeComponentPowerModel;
NodeComponentPowers=RAPL-->PodPowerRatio;
NodeComponentPowers=NodeComponentPowerModel-->PodPowerRatio;
NodeComponentPowerModel--not available-->PodTotalPowerModel,PodComponentPowerModel;
Power values is composed of If TotalPowerModel is not available, assume |
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In this post, I would like to discuss about the Power Model to be used in the Kepler by default for different measurement and node information availability.
In the Kepler Model Server, I proposed to have 8 types of trained power models.
The detailed explanation about each type is here.
To simplify,
Abs
refers to Node-level andDyn
refers to Pod-level.Power
refers to total power including non-ITPower andComponentPower
refers to core, dram.Weight
refers to weight for Linear Regression which can be directly applied without extra library and withoutWeight
refers to arbitrary model inference which requires kepler-estimator that comes with extra library.Thus, we have 8 combinations of the above choices.
My idea is to apply each combinations of {
Abs
,Dyn
} x {Power
,ComponentPower
} regarding the following scenarios.Note that, the choice of with
Weight
and withoutWeight
will be depend on model accuracy and inference overhead trade-off.We may consider with
Weight
by default.remarks:
I put Environment Info in optional as it might be the future extension of Kepler to read this value. If it becomes available, it can be a feature to make AbsPower model more accurate.
The training process (pipelines) of each model type is listed in the below table.
This is basically all for my current thought. It would be great if I could have your opinions about this direction.
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