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html/src/build.rst

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You will need suitable python and fortran environments. There is detailed help for
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* :doc:`OSX users <build/pre_osx>` (MAC M2)
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* :doc:`Linux users <build/pre_linux>` (MINT, UBUNTU)
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* :doc:`Conda users, works on both OSX and Linux <build/pre_conda>` (CONDA_OMPI_GNU)
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* :ref:`Linux MINT/UBUNTU users <buildlinux>`
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* :ref:`OSX users <buildosx>`
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* :ref:`Conda users <buildconda>`
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If you need more help, please raise an issue at https://github.com/gafusion/gacode/issues
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If you need more help, please don't hesitate to raise an issue at https://github.com/gafusion/gacode/issues.
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Source code
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-----------
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You should clone the gacode repository from GitHub. This was you can keep you installation up to date::
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You should clone the gacode repository from GitHub. This was you can keep your installation up to date
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$ git clone [email protected]:gafusion/gacode.git
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.. code::
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$ git clone [email protected]:gafusion/gacode.git
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Concept of platform files
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In order to compile the full GACODE package on a new machine, a version of the **platform files** must be selected. You can use existing ones or create your own. After cloning the repository, you can query the existing platforms using::
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In order to compile the full GACODE package on a new machine, a version of the **platform files** must be selected. You can use existing ones or create your own. After cloning the repository, you can query the existing platforms using
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$ ./gacode/shared/bin/gacode_platforms
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.. code::
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$ ./gacode/shared/bin/gacode_platforms
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If you decide to create new platform files (say, for a new platform MINE) then create the following files, starting from one of the existing ones (say, CONDA_OMPI_GNU)::
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html/src/input_gacode.rst

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input.gacode
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============
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.. |ip| replace:: :doc:`input.gacode <input_gacode>`
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To see what a sample |ip| looks like, at the command line type
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To see what a sample ``input.gacode`` looks like, at the command line type
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.. code:: bash
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$ cgyro -g reg14 ; cat reg14/input.gacode
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$ cgyro -g reg14 ; cat reg14/input.gacode
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Overview
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--------
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html/src/performance.rst

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Parallel Performance and Scalability
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\label{sec.performance}
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\vspace{-12pt}
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Strong-scaling performance
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^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. image:: images/performance/nl03_strong_frontier.png
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.. image:: images/performance/nl03_bar_frontier.png
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The (a) Multi-platform strong-scaling comparison for CGYRO test case \texttt{nl03}, showing wallclock time vs. number of nodes. Frontier is by far the best performer on both a per-node and maximum performance basis. (b) Kernel-level analysis.
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Left (darker) bars indicate compute time; right (faded) bars indicate the communication time. Data is normalized to the total time, such that total bar area is constant (1.0). Lower compute-to-communication ratio on GPU systems reflects the extremely high performance of the GPUs. Note the significant improvement in communication management from Summit/Perlmutter to Frontier.}}
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The (a) Multi-platform strong-scaling comparison for CGYRO test case \texttt{nl03}, showing wallclock time vs. number of nodes. Frontier is by far the best performer on both a per-node and maximum performance basis. (b) Kernel-level analysis. Left (darker) bars indicate compute time; right (faded) bars indicate the communication time. Data is normalized to the total time, such that total bar area is constant (1.0). Lower compute-to-communication ratio on GPU systems reflects the extremely high performance of the GPUs. Note the significant improvement in communication management from Summit/Perlmutter to Frontier.
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Kernel-based performance analysis

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