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# Learning phase
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This is a necessary phase prior to the normal EAR utilization and is a kind of hardware characterization of the nodes. During the phase a matrix of coefficients are calculated and stored. These coefficients will be used to predict the energy consumption and performance of each application.
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This is a necessary phase prior to the normal EAR utilization and is a kind of hardware characterization of the nodes. During the phase a matrix of coefficients are calculated and stored. These coefficients will be used to predict the energy consumption and performance of each application.
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Please, visit the learning phase [wiki page](https://gitlab.bsc.es/ear_team/ear_learning/-/wikis/home) to read the manual and the [repository](https://gitlab.bsc.es/ear_team/ear_learning) to get the scripts and the kernels.
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Please, visit the learning phase [wiki page](https://gitlab.bsc.es/ear_team/ear_learning/-/wikis/home) to read the manual and the [repository](https://gitlab.bsc.es/ear_team/ear_learning) to get the scripts and the kernels.
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## Tools
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# Tools
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The following table lists tools provided with EAR package to work with coefficients computed during the learning phase.
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The following table lists tools provided with EAR package to work with coefficients computed during the learning phase.
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... | @@ -17,7 +15,7 @@ The following table lists tools provided with EAR package to work with coefficie |
... | @@ -17,7 +15,7 @@ The following table lists tools provided with EAR package to work with coefficie |
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> Use the argument `--help` to expand the application information and list the admitted flags.
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> Use the argument `--help` to expand the application information and list the admitted flags.
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### Examples
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## Examples
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Compute the coefficients for the node `node1001` in which the minimum frequency set during the learning phase was 1900000 KHz
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Compute the coefficients for the node `node1001` in which the minimum frequency set during the learning phase was 1900000 KHz
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