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Code optimization based on source to source transformations using profile guided metrics

Youenn Lebras
Université de Versailles-Saint-Quentin-en-Yvelines
Université de Versailles-Saint-Quentin-en-Yvelines, 2019

@phdthesis{lebras2019code,

   title={Code optimization based on source to source transformations using profile guided metrics},

   author={LEBRAS, YOUENN},

   year={2019},

   school={Universit{‘e} d’Orleans}

}

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Modern high performance processor architectures tackle performance issues by heavily relying on increased vector lengths and advanced memory hierarchies to deliver high performance. Manual optimization is became a difficult task. Developers usually trust compilers to automatically address these performance issues, but they deploy static performance models and heuristics that force them to remain conservative. On the other hand, performance analysis tools are pretty good at detecting specific performance issues, but only return observations on the quality and on the execution of the code. Our goal is to develop a framework allowing to perform of source code transformations based on performance analysis tools metrics. This framework will be integrated into the MAQAO tool suite. We present an FDO tool with a set of source-to-source transformations guided by metrics coming from the various MAQAO tools and open to user advices. This framework can also be used to simplify the development by automatically performing some simple, but time-consuming and error-prone transformations (e.g. loop/function specialization).
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